Method and device of sustainably updating coefficient vector of finite impulse response filter

ABSTRACT

A method and a device of sustainably updating a coefficient vector of a finite impulse response FIRfilter. The method includes obtaining (21) a time-varying regularization factor used for iteratively updating the coefficient vector of the FIR filter in a case that the coefficient vector of the FIR filter is used for processing a preset signal; updating (22) the coefficient vector of the FIR filter according to the time-varying regularization factor.

CROSS-REFERENCE TO RELATED APPLICATION

This application is the U.S. national phase of PCT Application No.PCT/CN2018/101491 filed on Aug. 21, 2018, which claims priority to aChinese Patent Application No. 201710800778.4 filed in China on Sep. 7,2017, the disclosures of which are incorporated herein by reference intheir entireties.

TECHNICAL FIELD

The present disclosure relates to the field of a signal processingtechnique, and more particularly, relates to a method of sustainablyupdating a coefficient vector of a finite impulse response filter and adevice of sustainably updating a coefficient vector of a finite impulseresponse filter.

BACKGROUND

An Acoustic Echo Canceller (AEC) is a key component in a full-duplexvoice communication system, and a primary function of the AEC is toremove an echo signal of a far-end signal coupled by a speaker (horn)into a microphone. In the AEC, a path of an echo is adaptively modelledby learning with a finite impulse response (FIR) linear filter, and aneffective estimation value of the echo is synthesized therefrom. Theestimate which is then subtracted from a received signal of themicrophone, thereby completing a task of cancelling the echo. Inpresence of a near-end speech signal, since the near-end speech signalis not statistically correlated with the far-end speech signal, thenear-end speech signal behaves like a burst of noise, a result of whichis that a coefficient update of this filter will deviate from a truevalue corresponding to an actual path of the echo and thus diverge. Thiscorrespondingly increases an amount of residual echo and deterioratesperformance of the AEC. In view of this, it is firstly necessary to usea “double talk” detector (DTD) to timely and accurately detect whether anear-end speech signal (i.e., a “double talk” case) is contained in asignal received by the microphone. In a case where there is no near-endspeech signal (i.e., a “single talk” case) in the signal received by themicrophone, an adaptive learning process of linear filter coefficientscontinues; in a case where the signal received by the microphonecontains a near-end speech signal (i.e., the “double talk” case), theadaptive learning process of the linear filter coefficients must bestopped to avoid a divergence phenomenon caused by continuous learningof the filter coefficients in this case. However, a processing delay ofDTD and a possible misjudgment in the DTD will seriously affect anadaptive learning behavior of an AEC filter and further affect theperformance of the AEC. Therefore, a number of adaptive variablestep-size learning techniques are proposed to update iterativelycoefficients of FIR filters.

A disadvantage of adaptive variable step-size learning techniques isthat they are sensitive to parameter initialization. In addition, thistechnique is not suitable for dealing with “double talk” situationswhere there is near-end (non-stationary) spoken speech in an echocancellation application. Other schemes in the related art, such as aGeneralized Normalized Gradient Descent (GNGD) algorithm, also do notwork well in the “double talk” case with the near-end (non-stationary)spoken speech.

SUMMARY

Some embodiments of the present disclosure provide a method and a deviceof sustainably updating a coefficient vector of a Finite ImpulseResponse (FIR) filter, so as to solve a problem that an adaptivelearning mode of a relevant FIR filter cannot guarantee performancestability of the FIR filter and further affects signal processingreliability.

In a first aspect, some embodiments of the present disclosure provide asustainable adaptive updating method of a coefficient vector of a FiniteImpulse Response (FIR) filter. The method includes obtaining atime-varying regularization factor used for iteratively updating thecoefficient vector of the FIR filter in a case that the coefficientvector of the FIR filter is used for processing a preset signal;updating the coefficient vector of the FIR filter according to thetime-varying regularization factor.

Optionally, the preset signal includes one of combined pairs offollowing: a far-end reference speech signal inputted in an AcousticEcho Canceller (AEC) and a near-end speech signal received by amicrophone; a noise reference signal and a system input signal in anadaptive noise cancellation system; an interference reference signal anda system input signal in an adaptive interference cancellation system;and an excitation input signal and an unknown system output signal to beidentified in adaptive system identification.

Optionally, the preset signal includes a far-end reference speech signalinputted in an Acoustic Echo Canceller (AEC) and a near-end speechsignal received by a microphone; obtaining the time-varyingregularization factor used for iteratively updating the coefficientvector of the FIR filter in a case that the coefficient vector of theFIR filter is used for processing the preset signal, includes: obtaininga power of a signal received by a microphone and an effective estimationvalue of a coupling factor; according to the power of the signalreceived by the microphone and the effective estimation value of thecoupling factor, obtaining the time-varying regularization factor usedfor iteratively updating the coefficient vector of the FIR filter in acase that the coefficient vector of the FIR filter is used forprocessing the preset signal.

Optionally, a manner of obtaining the power of the signal received bythe microphone is: according to an Equation:

${{\hat{\sigma}}_{y}^{2}(t)} = \left\{ {\begin{matrix}{{{\alpha_{a} \cdot {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}} + {\left( {1 - \alpha_{a}} \right) \cdot {{y(t)}}^{2}}},{{{if}\mspace{14mu}{{y(t)}}^{2}} > {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}}} \\{{{\alpha_{d} \cdot {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}} + {\left( {1 - \alpha_{d}} \right) \cdot {{y(t)}}^{2}}},{{{if}\mspace{14mu}{{y(t)}}^{2}} > {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}}}\end{matrix},} \right.$obtaining the power of the signal received by the microphone; wherein{circumflex over (σ)}_(y) ²(t) is the power of the signal received bythe microphone; y(t) is the signal received by the microphone; α_(a) andα_(d) are preset recursive constant quantities, and 0≤α_(a)<α_(d)<1; tis a digital-signal time index number.

Optionally, a manner of obtaining the effective estimation value of thecoupling factor is: obtaining a biased estimation value of the couplingfactor according to a cross-correlation method; obtaining a correctionfactor used for compensating for the biased estimation value of thecoupling factor; obtaining the effective estimation value of thecoupling factor according to the biased estimation value of the couplingfactor and the correction factor.

Optionally, obtaining the biased estimation value of the coupling factoraccording to the cross-correlation method, includes: according to anEquation:

${{{\hat{\beta}(t)}❘_{{Cross}\text{-}{correlation}}} = \frac{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}\;{{e\left( {t - {t\; 1}} \right)}{x^{*}\left( {t - {t\; 1}} \right)}}}}^{2}}{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}\;{{x\left( {t - {t\; 1}} \right)}}^{2}}}^{2}}},$obtaining the biased estimation value of the coupling factor, wherein{circumflex over (β)}(t)|_(Cross-correlation) is the biased estimationvalue of the coupling factor based on a cross-correlation technique;x*(t−t1) is a complex conjugate of x(t−t1); t1=0, 1, 2, . . . , T_(s)−1,T_(s) is a quantity of samples used in estimation of the {circumflexover (β)}(t)|_(Cross-correlation), and T_(s)<<L, L is a quantity ofcoefficients of the FIR filter; e(t−t1) is an error signal outputted bythe AEC at a signal sample time (t−t1), e(t)=y(t)−{right arrow over(x)}^(H)(t){right arrow over (w)}(t), e(t) is an error signal outputtedby the AEC at a signal sample time t; y(t) is a signal received by themicrophone at a signal sample time t; {right arrow over (x)}^(H)(t) is aconjugate transpose matrix of {right arrow over (x)}(t); {right arrowover (x)}(t) is a far-end reference signal vector and {right arrow over(x)}(t)=[x(t), x(t−1), . . . , x(t−L+1)]^(T); x(t−t2) is a far-endreference signal at a signal sample time (t−t2); T is a transposeoperator; {right arrow over (w)}(t) is the coefficient vector of the FIRfilter, {right arrow over (w)}(t)=[w₀(t), w₁(t), . . . ,w_(L-1)(t)]^(T), w_(t2)(t) is a (t2+1)-th coefficient of the FIR filterat the signal sample time t, t2=0, 1, 2, . . . . , L−1; t is adigital-signal time index number.

Optionally, obtaining the correction factor used for compensating forthe biased estimation value of the coupling factor, includes: obtaininga candidate value of a square of a magnitude of a correlationcoefficient between an error signal outputted by the AEC and a far-endreference signal; obtaining, according to the candidate value of thesquare of the magnitude of the correlation coefficient between the errorsignal outputted by the AEC and the far-end reference signal, a squareof an effective magnitude of the correlation coefficient between theerror signal outputted by the AEC and the far-end reference signal, andtaking the square of the effective magnitude of the correlationcoefficient between the error signal outputted by the AEC and thefar-end reference signal as the correction factor used for compensatingfor the biased estimation value of the coupling factor.

Further, obtaining the candidate value of the square of the magnitude ofthe correlation coefficient between the error signal outputted by theAEC and the far-end reference signal, includes: according to anEquation:

${{\hat{r}(t)} = \frac{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}\;{{e\left( {t - {t\; 1}} \right)}{x^{*}\left( {t - {t\; 1}} \right)}}}}^{2}}{\left( {\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}\;{{e\left( {t - {t\; 1}} \right)}}^{2}} \right)\left( {\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}\;{{x\left( {t - {t\; 1}} \right)}}^{2}} \right)}},$obtaining the candidate value of the square of the magnitude of thecorrelation coefficient between the error signal outputted by the AECand the far-end reference signal; wherein {circumflex over (r)}(t) isthe candidate value of the square of the magnitude of the correlationcoefficient between the error signal outputted by the AEC and thefar-end reference signal; x(t−t1) is the far-end reference signal at asignal sample time (t−t1); x*(t−t1) is a complex conjugate of x(t−t1);t1=0, 1, 2, . . . , T_(s)−1, T_(s) is a quantity of samples used inestimation of {circumflex over (r)}(t), and T_(s)<<L, L is a quantity ofcoefficients of the FIR filter; e(t−t1) is the error signal outputted bythe AEC at a signal sample time (t−t1), e(t)=y(t)−{right arrow over(x)}^(H)(t){right arrow over (w)}(t), e(t) is an error signal outputtedby the AEC at a signal sample time t; y(t) is the signal received by themicrophone at the signal sample time t; {right arrow over (x)}^(H)(t) isa conjugate transpose matrix of {right arrow over (x)}(t); {right arrowover (x)}(t) is a far-end reference signal vector and {right arrow over(x)}(t)=[x(t), x(t−1), . . . , x(t−L+1)]^(T); x(t−t2) is the far-endreference signal at a signal sample time (t−t2); T is a transposeoperator; {right arrow over (w)}(t) is the coefficient vector of the FIRfilter, {right arrow over (w)}(t)=[w₀(t), w₁(t), . . . ,w_(L-1)(t)]^(T), w_(t2)(t) (t) is a (t2+1)-th coefficient of the FIRfilter at the signal sample time t, t2=0, 1, 2, . . . , L−1; t is adigital-signal sample time index number.

Optionally, obtaining, according to the candidate value of the square ofthe magnitude of the correlation coefficient between the error signaloutputted by the AEC and the far-end reference signal, the square of theeffective magnitude of the correlation coefficient between the errorsignal outputted by the AEC and the far-end reference signal, includes:according to an Equation:

${{{\hat{r}}_{ex}(t)}}^{2} = \left\{ {\begin{matrix}{{{\hat{r}(t)},}\mspace{79mu}} & {{{if}\mspace{14mu}{\hat{r}(t)}} > {{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2}} \\{{{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2},} & {{{if}\mspace{14mu}{\hat{r}(t)}} \leq {{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2}}\end{matrix},} \right.$obtaining the square of the effective magnitude of the correlationcoefficient between the signal outputted by the AEC and the far-endreference signal; wherein |{circumflex over (r)}_(ex)(t)|² is the squareof the effective magnitude of the correlation coefficient between theerror signal outputted by the AEC and the far-end reference signal;{circumflex over (r)}(t) is the candidate value of the square of themagnitude of the correlation coefficient between the error signaloutputted by the AEC and the far-end reference signal; t is adigital-signal sample time index number.

Optionally, obtaining the effective estimation value of the couplingfactor according to the biased estimation value of the coupling factorand the correction factor, includes: obtaining the effective estimationvalue of the coupling factor according to the Equation:

${{\hat{\beta}(t)} = \frac{{\hat{\beta}(t)}❘_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{ex}(t)}}^{2}}},$wherein {circumflex over (β)}(t)) is the effective estimation value ofthe coupling factor; {circumflex over (β)}(t)|_(Cross-correlation) isthe biased estimation value of the coupling factor based on across-correlation technique; |{circumflex over (r)}_(ex)(t)|² is thesquare of the effective magnitude of the correlation coefficient betweenthe error signal outputted by the AEC and the far-end reference signal;t is a digital-signal sample time index number.

Optionally, according to the power of the signal received by themicrophone and the effective estimation value of the coupling factor,obtaining the time-varying regularization factor used for iterativelyupdating the coefficient vector of the FIR filter in a case that thecoefficient vector of the FIR filter is used for processing the presetsignal, includes: according to an Equation:

${{\delta^{opt}(t)} = {{\max\left\{ {\frac{L \cdot {{\hat{\sigma}}_{y}^{2}(t)}}{{\hat{\beta}(t)} + \rho_{0}},\delta_{\min}} \right\}} = {\max\left\{ {\frac{{L \cdot {{\hat{\sigma}}_{y}^{2}(t)}}{{{\hat{r}}_{ex}(t)}}^{2}}{{\hat{\beta}(t)}❘_{{Cross}\text{-}{correlation}}{+ \rho}},\delta_{\min}} \right\}}}},$obtaining the time-varying regularization factor used for iterativelyupdating the coefficient vector of the FIR filter; wherein δ^(opt)(t) isthe time-varying regularization factor; L is a quantity of coefficientsof the FIR filter; {circumflex over (σ)}_(y) ²(t) is the power of thesignal received by the microphone;

${\hat{\beta}(t)} = \frac{{\hat{\beta}(t)}❘_{{Cross}\mspace{20mu}{correlation}}}{{{{\hat{r}}_{ex}(t)}}^{2}}$is the effective estimation value of coupling factor; {circumflex over(β)}(t)|_(Cross-correlation) is a biased estimation value of thecoupling factor based on a cross-correlation technique; |{circumflexover (r)}_(ex)(t)|² is a square of an effective magnitude of acorrelation coefficient between an error signal outputted by the AEC andthe far-end reference signal; δ_(min) is a preset small real constantquantity, and δ_(min)>0; ρ₀ and ρ are preset small real constants, andρ>0, ρ₀>0, respectively; t is a digital-signal sample time index number.

Optionally, updating the coefficient vector of the FIR filter accordingto the time-varying regularization factor, includes: according to anEquation: {right arrow over (w)}(t+1)={right arrow over (w)}(t)+μ·{rightarrow over (x)}(t)e*(t)/[{right arrow over (x)}^(H)(t){right arrow over(x)}(t)+δ^(opt)(t)], sustainably adaptively updating the coefficientvector of the FIR filter by applying a Normalized Least Mean Square(NLMS) algorithm, wherein, {right arrow over (w)}(t+1) is thecoefficient vector of the FIR filter after the coefficient vector of theFIR filter is updated; {right arrow over (w)}(t) is the coefficientvector of the FIR filter before the coefficient vector of the FIR filteris updated; μ is a predetermined coefficient updating step-sizeparameter, and 0<μ<2; {right arrow over (x)}(t) is a far-end referencesignal vector and {right arrow over (x)}(t)=[x(t), x(t−1), . . . ,x(t−L+1)]^(T); x(t−t2) is the far-end reference signal at a signalsample time (t−t2); T is a transpose operator; {right arrow over(x)}^(H)(t) is a conjugate transpose matrix of {right arrow over(x)}(t); δ^(opt)(t) is the time-varying regularization factor; e*(t) isa complex conjugate of e(t); e(t)=y(t)−{right arrow over(x)}^(H)(t){right arrow over (w)}(t), e(t) is the error signal outputtedby the AEC at a signal sample time t; y(t) is the signal received by themicrophone at the signal sample time t; {right arrow over(w)}(t)=[w₀(t), w₁(t), . . . , w_(L-1)(t)]^(T), w_(t2)(t) is a (t2+1)-thcoefficient of the FIR filter at the signal sample time t, t2=0, 1, 2, .. . , L−1; t is a digital-signal sample time index number.

Optionally, updating the coefficient vector of the FIR filter accordingto the time-varying regularization factor, includes: according to anEquation: {right arrow over (w)}(t+1)={right arrow over(w)}(t)+μ·X_(state)(t)[X_(state)^(H)(t)X_(state)(t)+δ^(opt)(t)·I_(P×P)]⁻¹·{right arrow over (e)}*(t)applying an affine projection (AP) algorithm to sustainably adaptivelyupdate the coefficient vector of the FIR filter; wherein {right arrowover (w)}(t+1) is the coefficient vector of the FIR filter after thecoefficient vector of the FIR filter is updated; {right arrow over(w)}(t) is the coefficient vector of the FIR filter before thecoefficient vector of the FIR filter is updated; μ is a predeterminedcoefficient updating step-size parameter, and 0<μ<2; δ^(opt)(t) is thetime-varying regularization factor; X_(state)(t) is L×P-dimension statematrix, and X_(state)(t)=[{right arrow over (x)}(t), {right arrow over(x)}(t−1), . . . , {right arrow over (x)}(t−P+1)]; {right arrow over(x)}(t−t3) is a far-end reference signal vector at a signal sample time(t−t3), and t3=0, 1, . . . , P−1, P is an order quantity of the APalgorithm; X_(state) ^(H)(t) is a conjugate transpose matrix ofX_(state)(t); I_(P×P) is a P×P-dimension unit matrix; {right arrow over(e)}*(t) is a complex conjugate of {right arrow over (e)}(t), and {rightarrow over (e)}(t)={right arrow over (y)}(t)−X_(state) ^(H)(t){rightarrow over (w)}(t); {right arrow over (e)}(t) is a P-dimension errorvector; {right arrow over (y)}(t) is a P-dimension vector of the signalreceived by the microphone, and {right arrow over (y)}(t)=[y(t), y(t−1),. . . , y(t−P+1)]^(T); y(t−t3) is the signal received by the microphoneat a signal sample time (t−t3); {right arrow over (w)}(t)=[w₀(t), w₁(t),. . . , w_(L-1)(t)]^(T), w_(t2)(t) is a (t2+1)-th coefficient of the FIRfilter at a signal sample time t, t2=0, 1, 2, . . . , L−1; t is adigital-signal sample time index number.

Specifically, the preset signal includes a subband spectrum of anear-end speech signal received by a microphone and inputted in anAcoustic Echo Canceller (AEC) and a subband spectrum of a far-endreference speech signal; the coefficient vector of the FIR filter is asubband-domain coefficient vector of the FIR filter and the time-varyingregularization factor is a subband-domain time-varying regularizationfactor. Obtaining the time-varying regularization factor used foriteratively updating the coefficient vector of the FIR filter in a casethat the coefficient vector of the FIR filter is used for processing thepreset signal, includes: obtaining a subband power spectrum of thesignal received by the microphone and an effective estimation value of asubband-domain coupling factor, respectively; according to the subbandpower spectrum of the signal received by the microphone and theeffective estimation value of the subband-domain coupling factor,obtaining a subband-domain time-varying regularization factor used foriteratively updating the subband-domain coefficient vector of the FIRfilter in a case that the subband-domain coefficient vector of the FIRfilter is used for processing a preset signal.

Optionally, a manner of obtaining the subband power spectrum of thesignal received by the microphone is: according to an Equation:

${{\hat{\sigma}}_{Y}^{2}\left( {k,n} \right)} = \left\{ {\begin{matrix}{{{\alpha_{a} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}} + {\left( {1 - \alpha_{a}} \right) \cdot {{Y\left( {k,n} \right)}}^{2}}},{{{if}\mspace{14mu}{{Y\left( {k,n} \right)}}^{2}} > {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}}} \\{{{\alpha_{d} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}} + {\left( {1 - \alpha_{d}} \right) \cdot {{Y\left( {k,n} \right)}}^{2}}},{{{if}\mspace{14mu}{{Y\left( {k,n} \right)}}^{2}} \leq {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}}}\end{matrix},} \right.$obtaining the subband power spectrum of the signal received by themicrophone; wherein {circumflex over (σ)}_(Y) ²(k, n) is the subbandpower spectrum of the signal received by the microphone; Y(k, n) is asubband spectrum of the signal received by the microphone; α_(a) andα_(d) are preset recursive constant quantities, and 0≤α_(a)<α_(d)<1; kis a subband index variable, k=0, 1, 2, . . . , K−1, and K is a totalquantity of subbands; n is a signal frame time index variable.

Optionally, a manner of obtaining the effective estimation value of thesubband-domain coupling factor, includes: obtaining a biased estimationvalue of the subband-domain coupling factor according to across-correlation method; obtaining a correction factor used forcompensating for the biased estimation value of the subband-domaincoupling factor; obtaining the effective estimation value of thesubband-domain coupling factor according to the biased estimation valueof the subband-domain coupling factor and the correction factor.

Optionally, obtaining the biased estimation value of the subband-domaincoupling factor according to the cross-correlation method, includes:according to an Equation:

${{{\hat{\beta}\left( {k,n} \right)}❘_{{Cross}\text{-}{correlation}}} = \frac{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}\;{{E\left( {k,{n - {n\; 1}}} \right)}{X^{*}\left( {k,{n - {n\; 1}}} \right)}}}}^{2}}{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}\;{{X\left( {k,{n - {n\; 1}}} \right)}}^{2}}}^{2}}},$obtaining the biased estimation value of the subband-domain couplingfactor; wherein {circumflex over (β)}(k, n)|_(Cross-correlation) is thebiased estimation value of the subband-domain coupling factor; X*(k,n−n1) is a complex conjugate of X(k, n−n1); n1=0, 1, 2, . . . , N_(s)−1,N_(s) is a quantity of signal frames used in estimation of {circumflexover (β)}(k, n)|_(Cross-correlation), and N_(s)<<L_(s), L_(s) is aquantity of coefficients of the FIR filter in each subband; E(k, n−n1)is a subband spectrum of an error signal outputted by the AEC at asignal frame time (n−n1); E(k, n)=Y(k, n)−X_(k) ^(H)(n){right arrow over(W)}_(k)(n), E(k, n) is the subband spectrum of the error signaloutputted by the AEC at a signal frame time n; Y(k, n) is the subbandspectrum of the signal received by the microphone at the signal frametime n; {right arrow over (X)}_(k) ^(H)(n) is a conjugate transposematrix of {right arrow over (X)}_(k)(n); {right arrow over (X)}_(k)(n)is a subband-spectrum vector of the far-end reference signal, and {rightarrow over (X)}_(k)(n)=[X(k, n), X(k, n−1), . . . , X(k,n−L_(s)+1)]^(T); X(k, n−n2) is a subband spectrum of a far-end referencesignal at a signal frame time (n−n2); T is a transpose operator; {rightarrow over (W)}_(k)(n) is the coefficient vector of the FIR filter in asubband k, {right arrow over (W)}_(k)(n)=[W₀(k, n), W₁(k, n), . . . ,W_(L) _(s) ₋₁(k, n)]^(T); {right arrow over (W)}_(n2)(k, n) is a(n2+1)-th coefficient of the FIR filter in the subband k at the signalframe time n, n2=0, 1, 2, . . . , Ls−1; k is a subband index variable,k=0, 1, 2, . . . , K−1, and K is a total quantity of subbands; n is asignal frame time index variable.

Optionally, obtaining the correction factor used for compensating forthe biased estimation value of the subband-domain coupling factor,includes: obtaining a candidate value of a square of a magnitude of acorrelation coefficient between a subband spectrum of an error signaloutputted by an AEC and a subband spectrum of a far-end referencesignal; obtaining, according to the candidate value of the square of themagnitude of the correlation coefficient between the subband spectrum ofthe error signal outputted by the AEC and the subband spectrum of thefar-end reference signal, a square of an effective magnitude of thecorrelation coefficient between the subband spectrum of the error signaloutputted by the AEC and the subband spectrum of the far-end referencesignal, and taking the square of the effective magnitude of thecorrelation coefficient between the subband spectrum of the error signaloutputted by the AEC and the subband spectrum of the far-end referencesignal as the correction factor used for compensating for the biasedestimation value of the subband-domain coupling factor.

Optionally, obtaining the candidate value of the square of the magnitudeof the correlation coefficient between the subband spectrum of the errorsignal outputted by the AEC and the subband spectrum of the far-endreference signal, includes: according to an Equation:

${{\hat{r}\left( {k,n} \right)} = \frac{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}\;{{E\left( {k,{n - {n\; 1}}} \right)}{X^{*}\left( {k,{n - {n\; 1}}} \right)}}}}^{2}}{{\left( {\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{E\left( {k,{n - {n\; 1}}} \right)}}^{2}} \right)\left( {\sum\limits_{m = 0}^{N_{s} - 1}\;{{X\left( {k,{n - {n\; 1}}} \right)}}^{2}} \right)}\;}},$obtaining the candidate value of the square of the magnitude of thecorrelation coefficient between the subband spectrum of the error signaloutputted by the AEC and the subband spectrum of the far-end referencesignal; wherein {circumflex over (r)}(k, n) is the candidate value ofthe square of the magnitude of the correlation coefficient between thesubband spectrum of the error signal outputted by the AEC and thesubband spectrum of the far-end reference signal; X*(k, n−n1) is acomplex conjugate of X(k, n−n1); n1=0, 1, 2, . . . , N_(s)−1, N_(s) is aquantity of frames used in estimation of {circumflex over (r)}(k, n),and N_(s)<<L_(s), L_(s) is a quantity of coefficients of the FIR filterin each subband; E(k, n−n1) is the subband spectrum of the error signaloutputted by the AEC at a signal frame time (n−n1); E(k, n)=Y(k,n)−{right arrow over (X)}_(k) ^(H)(n){right arrow over (W)}_(k)(n), E(k,n) is the subband spectrum of the error signal outputted by the AEC at asignal frame time n; Y(k, n) is a subband spectrum of a signal receivedby the microphone; {right arrow over (X)}_(k) ^(H)(n) is a conjugatetranspose matrix of {right arrow over (X)}_(k)(n); {right arrow over(X)}_(k)(n) is a subband spectrum vector of the far-end referencesignal, and {right arrow over (X)}_(k)(n)=[X(k, n), X(k, n−1), . . . ,X(k, n−L_(s)+1)]^(T); X(k, n−n2) is the subband spectrum of the far-endreference signal at the signal frame time (n−n2); T is a transposeoperator; {right arrow over (W)}_(k)(n) is the coefficient vector of theFIR filter in a subband k; {right arrow over (W)}_(k)(n)=[W₀(k, n),W₁(k, n), . . . , W_(L) _(s) ₋₁(k, n)]^(T); W_(n2)(k, n) is a (n2+1)-thcoefficient of the FIR filter in the signal frame time n in the subbandk; n2=0, 1, 2, . . . , Ls−1; k is a subband index variable; k=, 1, 2, .. . , K−1, and K is a total quantity of subbands; n is a signal frametime index variable.

Optionally, obtaining, according to the candidate value of the square ofthe magnitude of the correlation coefficient between the subbandspectrum of the error signal outputted by the AEC and the subbandspectrum of the far-end reference signal, the square of the effectivemagnitude of the correlation coefficient between the subband spectrum ofthe error signal outputted by the AEC and the subband spectrum of thefar-end reference signal, includes: according to an Equation:

${{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2} = \left\{ {\begin{matrix}{{{\hat{r}\left( {k,n} \right)},}} & {{{if}\mspace{14mu}{\hat{r}\left( {k,n} \right)}} > {{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2}} \\{{{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2},} & {{{if}\mspace{14mu}{\hat{r}\left( {k,n} \right)}} \leq {{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2}}\end{matrix},} \right.$obtaining the square of the effective magnitude of the correlationcoefficient between the subband spectrum of the error signal outputtedby the AEC and the subband spectrum of the far-end reference signal;wherein |{circumflex over (r)}_(EX)(k, n)|² is the square of theeffective magnitude of the correlation coefficient between the subbandspectrum of the error signal outputted by the AEC and the subbandspectrum of the far-end reference signal; {circumflex over (r)}(k, n) isthe candidate value of the square of the magnitude of the correlationcoefficient between the subband spectrum of the error signal outputtedby the AEC and the subband spectrum of the far-end reference signal; nis a signal frame time index variable.

Optionally, obtaining the effective estimation value of thesubband-domain coupling factor according to the biased estimation valueof the subband-domain coupling factor and the correction factor,includes: according to an Equation:

${{\hat{\beta}\left( {k,n} \right)} = \frac{{\hat{\beta}\left( {k,n} \right)}❘_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2}}},$obtaining the effective estimation value of the subband-domain couplingfactor; wherein {circumflex over (β)}(k, n) is the effective estimationvalue of the subband-domain coupling factor, {circumflex over (β)}(k,n)|_(Cross-correlation) is the biased estimation value of thesubband-domain coupling factor; |{circumflex over (r)}_(EX)(k, n)|² isthe square of the effective magnitude of the correlation coefficientbetween the subband spectrum of the error signal outputted by the AECand the subband spectrum of the far-end reference signal; n is a signalframe time index variable.

Optionally, according to the subband power spectrum of the signalreceived by the microphone and the effective estimation value of thesubband-domain coupling factor, obtaining the subband-domaintime-varying regularization factor used for iteratively updating thesubband-domain coefficient vector of the FIR filter in a case that thesubband-domain coefficient vector of the FIR filter is used forprocessing the reset signal, includes: according to an Equation:

${{\delta^{opt}\left( {k,n} \right)} = {{\max\left\{ {\frac{L_{s} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,n} \right)}}{{\hat{\beta}\left( {k,n} \right)} + \rho_{0}},\delta_{\min}} \right\}} = {\max\left\{ {\frac{{L_{s} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,n} \right)}}{{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2}}{{\hat{\beta}\left( {k,n} \right)}❘_{{Cross}\text{-}{correlation}}{+ \rho}},\delta_{\min}} \right\}}}},$obtaining the subband-domain time-varying regularization factor used foriteratively updating the subband-domain coefficient vector of the FIRfilter in a case that the subband-domain coefficient vector of the FIRfilter is used for processing the preset signal; wherein δ^(opt)(k, n)is the subband-domain time-varying regularization factor; {circumflexover (σ)}_(Y) ²(k, n) is a subband power spectrum of a signal receivedby the microphone;

${{\hat{\beta}\left( {k,n} \right)} = \frac{{\hat{\beta}\left( {k,n} \right)}❘_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2}}},$is the effective estimation value of the subband-domain coupling factor,{circumflex over (β)}(k, n)|_(Cross-correlation) is the biasedestimation value of the subband-domain coupling factor; |{circumflexover (r)}_(EX)(k, n)|² is the square of the effective magnitude of thecorrelation coefficient between the subband spectrum of the error signaloutputted by the AEC and the subband spectrum of the far-end referencesignal; δ_(min) is a preset small real constant quantity, and δ_(min)>0;ρ₀ and ρ are preset small real constants, and ρ>0, ρ₀>0; n is a signalframe time index variable.

Optionally, updating the coefficient vector of the FIR filter accordingto the time-varying regularization factor, includes: according to anEquation: {right arrow over (W)}_(k)(n+1)={right arrow over(W)}_(k)(n)+μ·{right arrow over (X)}_(k)(n)E*(k, n)/[{right arrow over(X)}_(k) ^(H)(n){right arrow over (X)}_(k)(f)+δ^(opt)(k, n)],sustainably adaptively updating the subband-domain coefficient vector ofthe FIR filter using a Normalized Least Mean Square (NLMS) algorithm;wherein {right arrow over (W)}_(k)(n+1) is a coefficient vector of theFIR filter in a subband k after the coefficient vector of the FIR filteris updated; {right arrow over (W)}_(k)(n) is the coefficient vector ofthe FIR filter in the subband k before the coefficient vector of the FIRfilter is updated; μ is a predetermined coefficient updating step-sizeparameter, and 0<μ<2; {right arrow over (X)}_(k)(n) is a subbandspectrum vector of the far-end reference signal; {right arrow over(X)}_(k)(n)=[X(k, n), X(k, n−1), . . . , X(k, n−L_(s)+1)]^(T); X(k,n−n2) is a subband spectrum of the far-end reference signal at a signalframe time (n−n2); n2=0, 1, . . . , Ls−1, Ls is a quantity ofcoefficients of the FIR filter in each subband, T is a transposeoperator; {right arrow over (X)}_(k) ^(H)(n) is a conjugate transposematrix of {right arrow over (X)}_(k)(n); E*(k, n) is a complex conjugateof E(k, n); E(k, n) is the subband spectrum of the error signaloutputted by AEC at a signal frame time n, and E(k, n)=Y(k, n)−{rightarrow over (X)}_(k)(n){right arrow over (W)}_(k)(n); Y(k, n) is thesubband spectrum of the signal received by the microphone at the signalframe time n; {right arrow over (W)}_(k)(n)=[W₀(k, n), W₁(k, n), . . . ,W_(L) _(s) ₋₁(k, n)]^(T); W_(n2)(k, n) is a (n2+1)-th coefficient of theFIR filter in the subband k at the signal frame time n; δ^(opt)(k, n) isthe subband-domain time-varying regularization factor; k is a subbandindex variable, k=0, 1, 2, . . . , K−1, and K is a total quantity ofsubbands; n is a signal frame time index variable.

Optionally, updating the coefficient vector of the FIR filter accordingto the time-varying regularization factor, includes: according to anEquation: {right arrow over (W)}_(k)(n+1)={right arrow over(W)}_(k)(n)+μ·X_(state)(k, n)·[X_(state) ^(H)(k, n)X_(state)(k,n)+δ^(opt)(k, n)·I_(P×P)]⁻¹·{right arrow over (E)}_(k)*(n), sustainablyadaptively updating the subband-domain coefficient vector of the FIRfilter using an affine projection (AP) algorithm; wherein {right arrowover (W)}_(k)(n+1) is the coefficient vector of the FIR filter in asubband k after the coefficient vector of the FIR filter is updated;{right arrow over (W)}_(k)(n) is the coefficient vector of the FIRfilter in the subband k before the coefficient vector of the FIR filteris updated; μ is a predetermined coefficient updating step-sizeparameter, and 0<μ<2; δ^(opt)(k, n) is the subband-domain time-varyingregularization factor; X_(state)(k, n) is an L×P-dimension state matrixin the subband k, and X_(state)(k, n)=[{right arrow over (X)}_(k)(n),{right arrow over (X)}_(k)(n−1), . . . , {right arrow over(X)}_(k)(n−P+1)]; {right arrow over (X)}_(k)(n−n3) is a subband spectrumvector of the far-end reference signal at a signal frame time (n−n3),and n3=0, 1, . . . , P−1, P is an order quantity of the AP algorithm;X_(state) ^(H)(k, n) is a conjugate transpose matrix of X_(state)(k, n);I_(P×P) is a P×P-dimension unit matrix; {right arrow over (E)}_(k)*(n)is a complex conjugate of {right arrow over (E)}_(k)(n), and {rightarrow over (E)}_(k)(n)={right arrow over (Y)}_(k)(n)−X_(state) ^(H)(k,n){right arrow over (W)}_(k)(n); {right arrow over (E)}_(k)(n) is aP-dimension subband spectrum vector of an error signal; {right arrowover (Y)}_(k)(n) is a P-dimension subband spectrum vector of a signalreceived by the microphone, and {right arrow over (Y)}_(k)(n)=[Y(k, n),Y(k, n−1), . . . , Y(k, n−P+1)]^(T); Y(k, n−n3) is the signal receivedby the microphone at a signal frame time (n−n3); {right arrow over(W)}_(k)(n)=[W₀(k, n), W₁(k, n), . . . , W_(L) _(s) ₋₁(k, n)]^(T);W_(n2)(k, n) is a (n2+1)-th coefficient of the FIR filter in the subbandk at a signal frame time n, n2=0, 1, . . . , Ls−1, Ls is a quantity ofcoefficients of the FIR filter in each subband; k is a subband indexvariable, k=0, 1, 2, . . . , K−1, and K is a total quantity of subbands;n is a signal frame time index variable.

In a second aspect, some embodiments of the present disclosure furtherprovide a sustainable adaptive updating device of a coefficient vectorof a Finite Impulse Response (FIR) filter. The device includes astorage, a processor, and a computer program stored on the storage andexecutable by the processor; wherein when the processor executes thecomputer program, the processor implements following steps: obtaining atime-varying regularization factor used for iteratively updating thecoefficient vector of the FIR filter in a case that the coefficientvector of the FIR filter is used for processing a preset signal;updating the coefficient vector of the FIR filter according to thetime-varying regularization factor.

Specifically, the preset signal includes one of combined pairs offollowing: a far-end reference speech signal inputted in an AcousticEcho Canceller (AEC) and a near-end speech signal received by amicrophone; a noise reference signal and a system input signal in anadaptive noise cancellation system; an interference reference signal anda system input signal in an adaptive interference cancellation system;and an excitation input signal and an unknown system output signal to beidentified in adaptive system identification.

Optionally, the preset signal includes a far-end reference speech signalinputted in an Acoustic Echo Canceller (AEC) and a near-end speechsignal received by a microphone; when the processor executes thecomputer program, the processor further implements following steps:obtaining a power of a signal received by a microphone and an effectiveestimation value of a coupling factor; according to the power of thesignal received by the microphone and the effective estimation value ofthe coupling factor, obtaining the time-varying regularization factorused for iteratively updating the coefficient vector of the FIR filterin a case that the coefficient vector of the FIR filter is used forprocessing the preset signal.

Optionally, when the processor executes the computer program, theprocessor further implements following steps: according to an Equation:

${{\hat{\sigma}}_{y}^{2}(t)} = \left\{ \begin{matrix}{{{\alpha_{a} \cdot {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}} + {\left( {1 - \alpha_{a}} \right) \cdot {{y(t)}}^{2}}},{{{if}\mspace{14mu}{{y(t)}}^{2}} > {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}}} \\{{{\alpha_{d} \cdot {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}} + {\left( {1 - \alpha_{d}} \right) \cdot {{y(t)}}^{2}}},{{{if}\mspace{14mu}{{y(t)}}^{2}} \leq {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}}}\end{matrix} \right.$obtaining the power of the signal received by the microphone; wherein{circumflex over (σ)}_(y) ²(t) is the power of the signal received bythe microphone; y(t) is the signal received by the microphone; α_(s) andα_(d) are preset recursive constant quantities, and 0≤α_(a)<α_(d)<1; tis a digital-signal time index number.

Optionally, when the processor executes the computer program, theprocessor further implements following steps: obtaining a biasedestimation value of the coupling factor according to a cross-correlationmethod; obtaining a correction factor used for compensating for thebiased estimation value of the coupling factor; obtaining the effectiveestimation value of the coupling factor according to the biasedestimation value of the coupling factor and the correction factor.

Optionally, when the processor executes the computer program, theprocessor further implements following steps: according to an Equation:

${{{\hat{\beta}(t)}❘_{{Cross}\text{-}{correlation}}} = \frac{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}\;{{e\left( {t - {t\; 1}} \right)}{x^{*}\left( {t - {t\; 1}} \right)}}}}^{2}}{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}\;{{x\left( {t - {t\; 1}} \right)}}^{2}}}^{2}}},$obtaining the biased estimation value of the coupling factor, wherein{circumflex over (β)}(t)|_(Cross-correlation) is the biased estimationvalue of the coupling factor based on a cross-correlation technique;x*(t−t1) is a complex conjugate of x(t−t1); t1=0, 1, 2, . . . , T_(s)−1,T_(s) is a quantity of samples used in estimation of the {circumflexover (β)}(t)|_(Cross-correlation), and T_(s)<<L, L is a quantity ofcoefficients of the FIR filter; e(t−t1) is an error signal outputted bythe AEC at a signal sample time (t−t1), e(t)=y(t)−{right arrow over(x)}^(H)(t){right arrow over (w)}(t), e(t) is an error signal outputtedby the AEC at a signal sample time t; y(t) is a signal received by themicrophone at a signal sample time t; {right arrow over (x)}^(H)(t) is aconjugate transpose matrix of {right arrow over (x)}(t); {right arrowover (x)}(t) is a far-end reference signal vector and {right arrow over(x)}(t)=[x(t), x(t−1), . . . , x(t−L+1)]^(T); x(t−t2) is a far-endreference signal at a signal sample time (t−t2); T is a transposeoperator; {right arrow over (w)}(t) is the coefficient vector of the FIRfilter, {right arrow over (w)}(t)=[w₀(t), w₁(t), . . . ,w_(L-1)(t)]^(T), w_(t2)(t) is a (t2+1)-th coefficient of the FIR filterat the signal sample time t, t2=0, 1, 2, . . . . , L−1; t is adigital-signal time index number.

Optionally, when the processor executes the computer program, theprocessor further implements following steps: obtaining a candidatevalue of a square of a magnitude of a correlation coefficient between anerror signal outputted by the AEC and a far-end reference signal;obtaining, according to the candidate value of the square of themagnitude of the correlation coefficient between the error signaloutputted by the AEC and the far-end reference signal, a square of aneffective magnitude of the correlation coefficient between the errorsignal outputted by the AEC and the far-end reference signal, and takingthe square of the effective magnitude of the correlation coefficientbetween the error signal outputted by the AEC and the far-end referencesignal as the correction factor used for compensating for the biasedestimation value of the coupling factor.

Optionally, when the processor executes the computer program, theprocessor further implements following steps: according to an Equation:

${{\hat{r}(t)} = \frac{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}\;{{t\left( {t - {t\; 1}} \right)}{x^{*}\left( {t - {t\; 1}} \right)}}}}^{2}}{{\left( {\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{e\left( {t - {t\; 1}} \right)}}^{2}} \right)\left( {\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}\;{{x\left( {t - {t\; 1}} \right)}}^{2}} \right)}\;}},$obtaining the candidate value of the square of the magnitude of thecorrelation coefficient between the error signal outputted by the AECand the far-end reference signal; wherein {circumflex over (r)}(t) isthe candidate value of the square of the magnitude of the correlationcoefficient between the error signal outputted by the AEC and thefar-end reference signal; x(t−t1) is the far-end reference signal at asignal sample time (t−t1); x*(t−t1) is a complex conjugate of x(t−t1);t1=0, 1, 2, . . . , T_(s)−1, T_(s) is a quantity of samples used inestimation of {circumflex over (r)}(t), and T_(s)<<L, L is a quantity ofcoefficients of the FIR filter; e(t−t1) is the error signal outputted bythe AEC at a signal sample time (t−t1), e(t)=y(t)−{right arrow over(x)}^(H)(t){right arrow over (w)}(t), e(t) is an error signal outputtedby the AEC at a signal sample time t; y(t) is the signal received by themicrophone at the signal sample time t; {right arrow over (x)}^(H)(t) isa conjugate transpose matrix of {right arrow over (x)}(t); {right arrowover (x)}(t) is a far-end reference signal vector and {right arrow over(x)}(t)=[x(t), x(t−1), . . . , x(t−L+1)]^(T); x(t−t2) is the far-endreference signal at a signal sample time (t−t2); T is a transposeoperator; {right arrow over (w)}(t) is the coefficient vector of the FIRfilter, {right arrow over (w)}(t)=[w₀(t), w₁(t), . . . ,w_(L-1)(t)]^(T), w_(t2)(t) (t) is a (t2+1)-th coefficient of the FIRfilter at the signal sample time t, t2=0, 1, 2, . . . , L−1; t is adigital-signal sample time index number.

Optionally, when the processor executes the computer program, theprocessor further implements following steps: according to an Equation:

${{{\hat{r}}_{ex}(t)}}^{2} = \left\{ \begin{matrix}{{{\hat{r}(t)},}\mspace{79mu}} & {{{if}\mspace{14mu}{\hat{r}(t)}} > {{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2}} \\{{{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2},} & {{{if}\mspace{14mu}{\hat{r}(t)}} \leq {{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2}}\end{matrix} \right.$obtaining the square of the effective magnitude of the correlationcoefficient between the signal outputted by the AEC and the far-endreference signal; wherein |{circumflex over (r)}_(ex)(t)|² is the squareof the effective magnitude of the correlation coefficient between theerror signal outputted by the AEC and the far-end reference signal;{circumflex over (r)}(t) is the candidate value of the square of themagnitude of the correlation coefficient between the error signaloutputted by the AEC and the far-end reference signal; t is adigital-signal sample time index number.

Optionally, when the processor executes the computer program, theprocessor further implements following steps: obtaining the effectiveestimation value of the coupling factor according to the Equation:

${{\hat{\beta}(t)} = \frac{{\hat{\beta}(t)}❘_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{ex}(t)}}^{2}}},$wherein {circumflex over (β)}(t)) is the effective estimation value ofthe coupling factor; {circumflex over (β)}(t)|_(Cross-correlation) isthe biased estimation value of the coupling factor based on across-correlation technique; |{right arrow over (r)}_(ex)(t)|² is thesquare of the effective magnitude of the correlation coefficient betweenthe error signal outputted by the AEC and the far-end reference signal;t is a digital-signal sample time index number.

Optionally, when the processor executes the computer program, theprocessor further implements following steps: according to an Equation:

${{\delta^{opt}(t)} = {{\max\left\{ {\frac{L \cdot {{\hat{\sigma}}_{y}^{2}(t)}}{{\hat{\beta}(t)} + \rho_{0}},\delta_{\min}} \right\}} = {\max\left\{ {\frac{{L \cdot {{\hat{\sigma}}_{y}^{2}(t)}}{{{\hat{r}}_{ex}(t)}}^{2}}{{\hat{\beta}(t)}❘_{{Cross}\text{-}{correlation}}{+ \rho}},\delta_{\min}} \right\}}}},$obtaining the time-varying regularization factor used for iterativelyupdating the coefficient vector of the FIR filter; wherein δ^(opt)(t) isthe time-varying regularization factor; L is a quantity of coefficientsof the FIR filter; {circumflex over (σ)}_(y) ²(t) is the power of thesignal received by the microphone;

${\hat{\beta}(t)} = \frac{{\hat{\beta}(t)}❘_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{ex}(t)}}^{2}}$is the effective estimation value of coupling factor; {circumflex over(β)}(t)|_(Cross-correlation) is a biased estimation value of thecoupling factor based on a cross-correlation technique; |{circumflexover (r)}_(ex)(t)|² is a square of an effective magnitude of acorrelation coefficient between an error signal outputted by the AEC andthe far-end reference signal; δ_(min) is a preset small real constantquantity, and δ_(min)>0; ρ₀ and ρ are preset small real constants, andρ>0, ρ₀>0, respectively; t is a digital-signal sample time index number.

Optionally, when the processor executes the computer program, theprocessor further implements following steps: according to an Equation:{right arrow over (w)}(t+1)={right arrow over (w)}(t)+μ·{right arrowover (x)}(t)e*(t)/[{right arrow over (x)}^(H)(t){right arrow over(x)}(t)+δ^(opt)(t)], sustainably adaptively updating the coefficientvector of the FIR filter by applying a Normalized Least Mean Square(NLMS) algorithm, wherein, {right arrow over (w)}(t+1) is thecoefficient vector of the FIR filter after the coefficient vector of theFIR filter is updated; {right arrow over (w)}(t) is the coefficientvector of the FIR filter before the coefficient vector of the FIR filteris updated; μ is a predetermined coefficient updating step-sizeparameter, and 0<μ<2; {right arrow over (x)}(t) is a far-end referencesignal vector and {right arrow over (x)}(t)=[x(t), x(t−1), . . . ,x(t−L+1)]^(T); x(t−t2) is the far-end reference signal at a signalsample time (t−t2); T is a transpose operator; {right arrow over(x)}^(H)(t) is a conjugate transpose matrix of {right arrow over(x)}(t); δ^(opt)(t) is the time-varying regularization factor; e*(t) isa complex conjugate of e(t); e(t)=y(t)−{right arrow over(x)}^(H)(t){right arrow over (w)}(t), e(t) is the error signal outputtedby the AEC at a signal sample time t; y(t) is the signal received by themicrophone at the signal sample time t; {right arrow over(w)}(t)=[w₀(t), w₁(t), . . . , w_(L-1)(t)]^(T), w_(t2)(t) is a (t2+1)-thcoefficient of the FIR filter at the signal sample time t, t2=0, 1, 2, .. . , L−1; t is a digital-signal sample time index number.

Optionally, when the processor executes the computer program, theprocessor further implements following steps: according to an Equation:{right arrow over (w)}(t+1)={right arrow over(w)}(t)+μ·X_(state)(t)[X_(state)^(H)(t)X_(state)(t)+δ^(opt)(t)·I_(P×P)]⁻¹·{right arrow over (e)}*(t),applying an affine projection (AP) algorithm to sustainably adaptivelyupdate the coefficient vector of the FIR filter; wherein {right arrowover (w)}(t+1) is the coefficient vector of the FIR filter after thecoefficient vector of the FIR filter is updated; {right arrow over(w)}(t) is the coefficient vector of the FIR filter before thecoefficient vector of the FIR filter is updated; μ is a predeterminedcoefficient updating step-size parameter, and 0<μ<2; δ^(opt)(t) is thetime-varying regularization factor; X_(state)(t) is L×P-dimension statematrix, and X_(state)(t)=[{right arrow over (x)}(t), {right arrow over(x)}(t−1), . . . , {right arrow over (x)}(t−P+1)]; {right arrow over(x)}(t−t3) is a far-end reference signal vector at a signal sample time(t−t3), and t3=0, 1, . . . , P−1, P is an order quantity of the APalgorithm; X_(state) ^(H)(t) is a conjugate transpose matrix ofX_(state)(t); I_(P×P) is a P×P-dimension unit matrix; {right arrow over(e)}*(t) is a complex conjugate of {right arrow over (e)}(t), and {rightarrow over (e)}(t)={right arrow over (y)}(t)−X_(state) ^(H)(t){rightarrow over (w)}(t); {right arrow over (e)}(t) is a P-dimension errorvector; {right arrow over (y)}(t) is a P-dimension vector of the signalreceived by the microphone, and {right arrow over (y)}(t)=[y(t), y(t−1),. . . , y(t−P+1)]^(T); y(t−t3) is the signal received by the microphoneat a signal sample time (t−t3); {right arrow over (w)}(t)=[w₀(t), w₁(t),. . . , w_(L-1)(t)]^(T), w_(t2)(t) is a (t2+1)-th coefficient of the FIRfilter at a signal sample time t, t2=0, 1, 2, . . . , L−1; t is adigital-signal sample time index number.

Optionally, the preset signal includes a subband spectrum of a near-endspeech signal received by a microphone and inputted in an Acoustic EchoCanceller (AEC) and a subband spectrum of a far-end reference speechsignal; the coefficient vector of the FIR filter is a subband-domaincoefficient vector of the FIR filter and the time-varying regularizationfactor is a subband-domain time-varying regularization factor. When theprocessor executes the computer program, the processor furtherimplements following steps: obtaining a subband power spectrum of thesignal received by the microphone and an effective estimation value of asubband-domain coupling factor, respectively; according to the subbandpower spectrum of the signal received by the microphone and theeffective estimation value of the subband-domain coupling factor,obtaining a subband-domain time-varying regularization factor used foriteratively updating the subband-domain coefficient vector of the FIRfilter in a case that the subband-domain coefficient vector of the FIRfilter is used for processing a preset signal.

Optionally, when the processor executes the computer program, theprocessor further implements following steps: according to an Equation:

${{\hat{\sigma}}_{Y}^{2}\left( {k,n} \right)} = \left\{ {\begin{matrix}{{{\alpha_{a} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}} + {\left( {1 - \alpha_{a}} \right) \cdot {{Y\left( {k,n} \right)}}^{2}}},{{{if}\mspace{14mu}{{Y\left( {k,n} \right)}}^{2}} > {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}}} \\{{{\alpha_{d} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}} + {\left( {1 - \alpha_{d}} \right) \cdot {{Y\left( {k,n} \right)}}^{2}}},{{{if}\mspace{14mu}{{Y\left( {k,n} \right)}}^{2}} \leq {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}}}\end{matrix},} \right.$obtaining the subband power spectrum of the signal received by themicrophone; wherein {circumflex over (σ)}_(Y) ²(k, n) is the subbandpower spectrum of the signal received by the microphone; Y(k, n) is asubband spectrum of the signal received by the microphone; α_(a) andα_(d) are preset recursive constant quantities, and 0≤α_(a)<α_(d)<1; kis a subband index variable, k=0, 1, 2, . . . , K−1, and K is a totalquantity of subbands; n is a signal frame time index variable.

Optionally, when the processor executes the computer program, theprocessor further implements following steps: obtaining a biasedestimation value of the subband-domain coupling factor according to across-correlation method; obtaining a correction factor used forcompensating for the biased estimation value of the subband-domaincoupling factor; obtaining the effective estimation value of thesubband-domain coupling factor according to the biased estimation valueof the subband-domain coupling factor and the correction factor.

Optionally, when the processor executes the computer program, theprocessor further implements following steps: according to an Equation:

${{{\hat{\beta}\left( {k,n} \right)}❘_{{Cross}\text{-}{correlation}}} = \frac{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}\;{{E\left( {k,{n - {n\; 1}}} \right)}{X^{*}\left( {k,{n - {n\; 1}}} \right)}}}}^{2}}{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}\;{{X\left( {k,{n - {n\; 1}}} \right)}}^{2}}}^{2}}},$obtaining the biased estimation value of the subband-domain couplingfactor; wherein {circumflex over (β)}(k, n)|_(Cross-correlation) is thebiased estimation value of the subband-domain coupling factor; X*(k,n−n1) is a complex conjugate of X(k, n−n1); n1=0, 1, 2, . . . , N_(s)−1,N_(s) is a quantity of signal frames used in estimation of {circumflexover (β)}(k, n)|_(Cross-correlation), and N_(s)<<L_(s), L_(s) is aquantity of coefficients of the FIR filter in each subband; E(k, n−n1)is a subband spectrum of an error signal outputted by the AEC at asignal frame time (n−n1); E(k, n)=Y(k, n)−{right arrow over (X)}_(k)^(H)(n){right arrow over (W)}_(k)(n), E(k, n) is the subband spectrum ofthe error signal outputted by the AEC at a signal frame time n; Y(k, n)is the subband spectrum of the signal received by the microphone at thesignal frame time n; {right arrow over (X)}_(k) ^(H)(n) is a conjugatetranspose matrix of {right arrow over (X)}_(k)(n); {right arrow over(X)}_(k)(n) is a subband-spectrum vector of the far-end referencesignal, and {right arrow over (X)}_(k)(n)=[X(k, n), X(k, n−1), . . . ,X(k, n−L_(s)+1)]^(T); X(k, n−n2) is a subband spectrum of a far-endreference signal at a signal frame time (n−n2); T is a transposeoperator; {right arrow over (W)}_(k)(n) is the coefficient vector of theFIR filter in a subband k, {right arrow over (W)}_(k)(n)=[W₀(k, n),W₁(k, n), . . . , W_(L) _(s) ₋₁(k, n)]^(T); W_(n2)(k, n) is a (n2+1)-thcoefficient of the FIR filter in the subband k at the signal frame timen, n2=0, 1, 2, . . . , Ls−1; k is a subband index variable, k=0, 1, 2, .. . , K−1, and K is a total quantity of subbands; n is a signal frametime index variable.

Optionally, when the processor executes the computer program, theprocessor further implements following steps: obtaining a candidatevalue of a square of a magnitude of a correlation coefficient between asubband spectrum of an error signal outputted by an AEC and a subbandspectrum of a far-end reference signal; obtaining, according to thecandidate value of the square of the magnitude of the correlationcoefficient between the subband spectrum of the error signal outputtedby the AEC and the subband spectrum of the far-end reference signal, asquare of an effective magnitude of the correlation coefficient betweenthe subband spectrum of the error signal outputted by the AEC and thesubband spectrum of the far-end reference signal, and taking the squareof the effective magnitude of the correlation coefficient between thesubband spectrum of the error signal outputted by the AEC and thesubband spectrum of the far-end reference signal as the correctionfactor used for compensating for the biased estimation value of thesubband-domain coupling factor.

Optionally, when the processor executes the computer program, theprocessor further implements following steps: according to an Equation:

${{\hat{r}\left( {k,n} \right)} = \frac{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}\;{{E\left( {k,{n - {n\; 1}}} \right)}{X^{*}\left( {k,{n - {n\; 1}}} \right)}}}}^{2}}{\left( {\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}\;{{E\left( {k,{n - {n\; 1}}} \right)}}^{2}} \right)\left( {\sum\limits_{m = 0}^{N_{s} - 1}\;{{X\left( {k,{n - {n\; 1}}} \right)}}^{2}} \right)}},$obtaining the candidate value of the square of the magnitude of thecorrelation coefficient between the subband spectrum of the error signaloutputted by the AEC and the subband spectrum of the far-end referencesignal; wherein {circumflex over (r)}(k, n) is the candidate value ofthe square of the magnitude of the correlation coefficient between thesubband spectrum of the error signal outputted by the AEC and thesubband spectrum of the far-end reference signal; X*(k, n−n1) is acomplex conjugate of X(k, n−n1); n1=0, 1, 2, . . . , N_(s)−1, N_(s) is aquantity of frames used in estimation of {circumflex over (r)}(k, n),and N_(s)<<L_(s), L_(s) is a quantity of coefficients of the FIR filterin each subband; E(k, n−n1) is the subband spectrum of the error signaloutputted by the AEC at a signal frame time (n−n1); E(k, n)=Y(k,n)−{right arrow over (X)}_(k) ^(H)(n){right arrow over (W)}_(k)(n), E(k,n) is the subband spectrum of the error signal outputted by the AEC at asignal frame time n; Y(k, n) is a subband spectrum of a signal receivedby the microphone; {right arrow over (X)}_(k) ^(H)(n) is a conjugatetranspose matrix of {right arrow over (X)}_(k)(n); {right arrow over(X)}_(k)(n) is a subband spectrum vector of the far-end referencesignal, and {right arrow over (X)}_(k)(n)=[X(k, n), X(k, n−1), . . . ,X(k, n−L_(s)+1)]^(T); X(k, n−n2) is the subband spectrum of the far-endreference signal at the signal frame time (n−n2); T is a transposeoperator; {right arrow over (W)}_(k)(n) is the coefficient vector of theFIR filter in a subband k; {right arrow over (W)}_(k)(n)=[W₀(k, n),W₁(k, n), . . . , W_(L) _(s) ₋₁(k, n)]^(T); W_(n2)(k, n) is a (n2+1)-thcoefficient of the FIR filter in the signal frame time n in the subbandk; n2=0, 1, 2, . . . , Ls−1; k is a subband index variable; k=, 1, 2, .. . , K−1, and K is a total quantity of subbands; n is a signal frametime index variable.

Optionally, when the processor executes the computer program, theprocessor further implements following steps: according to an Equation:

${{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2} = \left\{ {\begin{matrix}{{{\hat{r}\left( {k,n} \right)},}} & {{{if}\mspace{14mu}{\hat{r}\left( {k,n} \right)}} > {{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2}} \\{{{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2},} & {{{if}\mspace{14mu}{\hat{r}\left( {k,n} \right)}} \leq {{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2}}\end{matrix},} \right.$obtaining the square of the effective magnitude of the correlationcoefficient between the subband spectrum of the error signal outputtedby the AEC and the subband spectrum of the far-end reference signal;wherein |{circumflex over (r)}_(EX)(k, n)|² is the square of theeffective magnitude of the correlation coefficient between the subbandspectrum of the error signal outputted by the AEC and the subbandspectrum of the far-end reference signal; {circumflex over (r)}(k, n) isthe candidate value of the square of the magnitude of the correlationcoefficient between the subband spectrum of the error signal outputtedby the AEC and the subband spectrum of the far-end reference signal; nis a signal frame time index variable.

Optionally, when the processor executes the computer program, theprocessor further implements following steps: according to an Equation:

${{\hat{\beta}\left( {k,n} \right)} = \frac{{\hat{\beta}\left( {k,n} \right)}❘_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2}}},$obtaining the effective estimation value of the subband-domain couplingfactor; wherein {circumflex over (β)}(k, n) is the effective estimationvalue of the subband-domain coupling factor, {circumflex over (β)}(k,n)|_(Cross-correlation) is the biased estimation value of thesubband-domain coupling factor; |{circumflex over (r)}_(EX)(k, n)|² isthe square of the effective magnitude of the correlation coefficientbetween the subband spectrum of the error signal outputted by the AECand the subband spectrum of the far-end reference signal; n is a signalframe time index variable.

Optionally, when the processor executes the computer program, theprocessor further implements following steps: according to an Equation:

${{\delta^{opt}\left( {k,n} \right)} = {{\max\left\{ {\frac{L_{s} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,n} \right)}}{{\hat{\beta}\left( {k,n} \right)} + \rho_{0}},\delta_{\min}} \right\}} = {\max\left\{ {\frac{{L_{s} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,n} \right)}}{{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2}}{{\hat{\beta}\left( {k,n} \right)}❘_{{Cross}\text{-}{correlation}}{+ \rho}},\delta_{\min}} \right\}}}},$obtaining the subband-domain time-varying regularization factor used foriteratively updating the subband-domain coefficient vector of the FIRfilter in a case that the subband-domain coefficient vector of the FIRfilter is used for processing the preset signal; wherein δ^(opt)(k, n)is the subband-domain time-varying regularization factor; {circumflexover (σ)}_(Y) ²(k, n) is a subband power spectrum of a signal receivedby the microphone;

${\hat{\beta}\left( {k,n} \right)} = \frac{{\hat{\beta}\left( {k,n} \right)}❘_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2}}$is the effective estimation value of the subband-domain coupling factor,{circumflex over (β)}(k, n)|_(Cross-correlation) is the biasedestimation value of the subband-domain coupling factor; |{circumflexover (r)}_(EX)(k, n)|² is the square of the effective magnitude of thecorrelation coefficient between the subband spectrum of the error signaloutputted by the AEC and the subband spectrum of the far-end referencesignal; δ_(min) is a preset small real constant quantity, and δ_(min)>0;ρ₀ and ρ are preset small real constants, and ρ>0, ρ₀>0; n is a signalframe time index variable.

Optionally, when the processor executes the computer program, theprocessor further implements following steps: according to an Equation:{right arrow over (W)}_(k)(n+1)={right arrow over (W)}_(k)(n)+μ·{rightarrow over (X)}_(k)(n)E*(k, n)/[{right arrow over (X)}_(k) ^(H)(n){rightarrow over (X)}_(k)(n)+δ^(opt)(k, n)], sustainably adaptively updatingthe subband-domain coefficient vector of the FIR filter using aNormalized Least Mean Square (NLMS) algorithm; wherein {right arrow over(W)}_(k)(n+1) is a coefficient vector of the FIR filter in a subband kafter the coefficient vector of the FIR filter is updated; {right arrowover (W)}_(k)(n) is the coefficient vector of the FIR filter in thesubband k before the coefficient vector of the FIR filter is updated; μis a predetermined coefficient updating step-size parameter, and 0<μ<2;{right arrow over (X)}_(k)(n) is a subband spectrum vector of thefar-end reference signal; {right arrow over (X)}_(k)(n)=[X(k, n), X(k,n−1), . . . , X(k, n−L_(s)+1)]^(T); X(k, n−n2) is a subband spectrum ofthe far-end reference signal at a signal frame time (n−n2); n2=0, 1, . .. , Ls−1, Ls is a quantity of coefficients of the FIR filter in eachsubband, T is a transpose operator; {right arrow over (X)}_(k) ^(H)(n)is a conjugate transpose matrix of {right arrow over (X)}_(k)(n); E*(k,n) is a complex conjugate of E(k, n); E(k, n) is the subband spectrum ofthe error signal outputted by AEC at a signal frame time n, and E(k,n)=Y(k, n)−{right arrow over (X)}_(k) ^(H)(n){right arrow over(W)}_(k)(n); Y(k, n) is the subband spectrum of the signal received bythe microphone at the signal frame time n; {right arrow over(W)}_(k)(n)=[W₀(k, n), W₁(k, n), . . . , W_(L) _(s) ₋₁(k, n)]^(T);W_(n2)(k, n) is a (n2+1)-th coefficient of the FIR filter in the subbandk at the signal frame time n; δ^(opt)(k, n) is the subband-domaintime-varying regularization factor; k is a subband index variable, k=0,1, 2, . . . , K−1, and K is a total quantity of subbands; n is a signalframe time index variable.

Optionally, when the processor executes the computer program, theprocessor further implements following steps: according to an Equation:{right arrow over (W)}_(k)(n+1)={right arrow over(W)}_(k)(n)+μ·X_(state)(k, n)·[X_(state) ^(H)(k, n)X_(state)(k,n)+δ^(opt)(k, n)·I_(P×P)]⁻¹·{right arrow over (E)}_(k)*(n), substainablyadaptively updating the subband-domain coefficient vector of the FIRfilter using an affine projection (AP) algorithm; wherein {right arrowover (W)}_(k)(n+1) is the coefficient vector of the FIR filter in asubband k after the coefficient vector of the FIR filter is updated;{right arrow over (W)}_(k)(n) is the coefficient vector of the FIRfilter in the subband k before the coefficient vector of the FIR filteris updated; μ is a predetermined coefficient updating step-sizeparameter, and 0<μ<2; δ^(opt)(k, n) is the subband-domain time-varyingregularization factor; X_(state)(k, n) is an L×P-dimension state matrixin the subband k, and X_(state)(k, n)=[{right arrow over (X)}_(k)(n),{right arrow over (X)}_(k)(n−1), . . . , {right arrow over(X)}_(k)(n−P+1)]; {right arrow over (X)}_(k)(n−n3) is a subband spectrumvector of the far-end reference signal at a signal frame time (n−n3),and n3=0, 1, . . . , P−1, P is an order quantity of the AP algorithm;X_(state) ^(H)(k, n) is a conjugate transpose matrix of X_(state)(k, n);I_(P×P) is a P×P-dimension unit matrix; {right arrow over (E)}_(k)*(n)is a complex conjugate of {right arrow over (E)}_(k)(n), and {rightarrow over (E)}_(k)(n)={right arrow over (Y)}_(k)(n)−X_(state) ^(H)(k,n){right arrow over (W)}_(k)(n); {right arrow over (E)}_(k)(n) is aP-dimension subband spectrum vector of an error signal; {right arrowover (Y)}_(k)(n) is a P-dimension subband spectrum vector of a signalreceived by the microphone, and {right arrow over (Y)}_(k)(n)=[Y(k, n),Y(k, n−1), . . . , Y(k, n−P+1)]^(T); Y(k, n−n3) is the signal receivedby the microphone at a signal frame time (n−n3); {right arrow over(W)}_(k)(n)=[W₀(k, n), W₁(k, n), . . . , W_(L) _(s) ₋₁(k, n)]^(T);W_(n2)(k, n) is a (n2+1)-th coefficient of the FIR filter in the subbandk at a signal frame time n, n2=0, 1, . . . , Ls−1, Ls is a quantity ofcoefficients of the FIR filter in each subband; k is a subband indexvariable, k=0, 1, 2, . . . , K−1, and K is a total quantity of subbands;n is a signal frame time index variable.

In a third aspect, some embodiments of the present disclosure furtherprovide a computer readable storage medium. The medium includes acomputer program stored on the computer readable storage medium, whereinwhen a processor executes the computer program, the processor implementsfollowing steps: obtaining a time-varying regularization factor used foriteratively updating a coefficient vector of a Finite Impulse Response(FIR) filter in a case that the coefficient vector of the FIR filter isused for processing a preset signal; updating the coefficient vector ofthe FIR filter according to the time-varying regularization factor.

In a fourth aspect, some embodiments of the present disclosure furtherprovide a sustainable adaptive updating device of a coefficient vectorof a Finite Impulse Response (FIR) filter. The device includes anobtaining module, configured to obtain a time-varying regularizationfactor used for iteratively updating the coefficient vector of the FIRfilter in a case that the coefficient vector of the FIR filter is usedfor processing a preset signal; an updating module, configured to updatethe coefficient vector of the FIR filter according to the time-varyingregularization factor.

Optionally, the preset signal includes a far-end reference speech signalinputted in an Acoustic Echo Canceller (AEC) and a near-end speechsignal received by a microphone. The obtaining module includes: a firstobtaining unit, configured to obtain a power of a signal received by themicrophone and an effective estimation value of a coupling factor; asecond obtaining unit, configured to, according to the power of thesignal received by the microphone and the effective estimation value ofthe coupling factor, obtain the time-varying regularization factor usedfor iteratively updating the coefficient vector of the FIR filter in acase that the coefficient vector of the FIR filter is used forprocessing the preset signal.

Optionally, a manner of obtaining the power of the signal received bythe microphone performed by the first obtaining unit is: according to anEquation:

${{\hat{\sigma}}_{y}^{2}(t)} = \left\{ \begin{matrix}{{{\alpha_{a} \cdot {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}} + {\left( {1 - \alpha_{a}} \right) \cdot {{y(t)}}^{2}}},{{{if}\mspace{14mu}{{y(t)}}^{2}} > {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}}} \\{{{\alpha_{d} \cdot {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}} + {\left( {1 - \alpha_{d}} \right) \cdot {{y(t)}}^{2}}},{{{if}\mspace{14mu}{{y(t)}}^{2}} \leq {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}}}\end{matrix} \right.$obtaining the power of the signal received by the microphone; wherein{circumflex over (σ)}_(y) ²(t) is the power of the signal received bythe microphone; y(t) is the signal received by the microphone; α_(a) andα_(d) are preset recursive constant quantities, and 0≤α_(a)<α_(d)<1; tis a digital-signal time index number.

Optionally, a manner of obtaining the effective estimation value of thecoupling factor performed by the first obtaining unit is: obtaining abiased estimation value of the coupling factor according to across-correlation method; obtaining a correction factor used forcompensating for the biased estimation value of the coupling factor;obtaining the effective estimation value of the coupling factoraccording to the biased estimation value of the coupling factor and thecorrection factor.

Optionally, a manner of obtaining the biased estimation value of thecoupling factor according to the cross-correlation method is: accordingto an Equation:

${{{\hat{\beta}(t)}❘_{{Cross}\text{-}{correlation}}} = \frac{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}\;{{e\left( {t - {t\; 1}} \right)}{x^{*}\left( {t - {t\; 1}} \right)}}}}^{2}}{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}\;{{x\left( {t - {t\; 1}} \right)}}^{2}}}^{2}}},$obtaining the biased estimation value of the coupling factor, wherein{circumflex over (β)}(t)|_(Cross-correlation) is the biased estimationvalue of the coupling factor based on a cross-correlation technique;x*(t−t1) is a complex conjugate of x(t−t1); t1=0, 1, 2, . . . , T_(s)−1,T_(s) is a quantity of samples used in estimation of the {circumflexover (β)}(t)|_(Cross-correlation), and T_(s)<<L, L is a quantity ofcoefficients of the FIR filter; e(t−t1) is an error signal outputted bythe AEC at a signal sample time (t−t1), e(t)=y(t)−{right arrow over(x)}^(H)(t){right arrow over (w)}(t), e(t) is an error signal outputtedby the AEC at a signal sample time t; y(t) is a signal received by themicrophone at a signal sample time t; {right arrow over (x)}^(H)(t) is aconjugate transpose matrix of {right arrow over (x)}(t); {right arrowover (x)}(t) is a far-end reference signal vector and {right arrow over(x)}(t)=[x(t), x(t−1), . . . , x(t−L+1)]^(T); x(t−t2) is a far-endreference signal at a signal sample time (t−t2); T is a transposeoperator; {right arrow over (w)}(t) is the coefficient vector of the FIRfilter, {right arrow over (w)}(t)=[w₀(t), w₁(t), . . . ,w_(L-1)(t)]^(T), w_(t2)(t) is a (t2+1)-th coefficient of the FIR filterat the signal sample time t, t2=0, 1, 2, . . . . , L−1; t is adigital-signal time index number.

Optionally, a manner of obtaining the correction factor used forcompensating for the biased estimation value of the coupling factor is:obtaining a candidate value of a square of a magnitude of a correlationcoefficient between an error signal outputted by the AEC and a far-endreference signal; obtaining, according to the candidate value of thesquare of the magnitude of the correlation coefficient between the errorsignal outputted by the AEC and the far-end reference signal, a squareof an effective magnitude of the correlation coefficient between theerror signal outputted by the AEC and the far-end reference signal, andtaking the square of the effective magnitude of the correlationcoefficient between the error signal outputted by the AEC and thefar-end reference signal as the correction factor used for compensatingfor the biased estimation value of the coupling factor.

Optionally, a manner of obtaining the candidate value of the square ofthe magnitude of the correlation coefficient between the error signaloutputted by the AEC and the far-end reference signal is: according toan Equation:

${{\hat{r}(t)} = \frac{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}\;{{e\left( {t - {t\; 1}} \right)}{x^{*}\left( {t - {t\; 1}} \right)}}}}^{2}}{\left( {\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}\;{{e\left( {t - {t\; 1}} \right)}}^{2}} \right)\left( {\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}\;{{x\left( {t - {t\; 1}} \right)}}^{2}} \right)}},$obtaining the candidate value of the square of the magnitude of thecorrelation coefficient between the error signal outputted by the AECand the far-end reference signal; wherein {circumflex over (r)}(t) isthe candidate value of the square of the magnitude of the correlationcoefficient between the error signal outputted by the AEC and thefar-end reference signal; x(t−t1) is the far-end reference signal at asignal sample time (t−t1); x*(t−t1) is a complex conjugate of x(t−t1);t1=0, 1, 2, . . . , T_(s)−1, T_(s) is a quantity of samples used inestimation of {circumflex over (r)}(t), and T_(s)<<L, L is a quantity ofcoefficients of the FIR filter; e(t−t1) is the error signal outputted bythe AEC at a signal sample time (t−t1), e(t)=y(t)−{right arrow over(x)}^(H)(t){right arrow over (w)}(t), e(t) is an error signal outputtedby the AEC at a signal sample time t; y(t) is the signal received by themicrophone at the signal sample time t; {right arrow over (x)}^(H)(t) isa conjugate transpose matrix of {right arrow over (x)}(t); {right arrowover (x)}(t) is a far-end reference signal vector and {right arrow over(x)}(t)=[x(t), x(t−1), . . . , x(t−L+1)]^(T); x(t−t2) is the far-endreference signal at a signal sample time (t−t2); T is a transposeoperator; {right arrow over (w)}(t) is the coefficient vector of the FIRfilter, {right arrow over (w)}(t)=[w₀(t), w₁(t), . . . ,w_(L-1)(t)]^(T), w_(t2)(t) (t) is a (t2+1)-th coefficient of the FIRfilter at the signal sample time t, t2=0, 1, 2, . . . , L−1; t is adigital-signal sample time index number.

Optionally, a manner of obtaining, according to the candidate value ofthe square of the magnitude of the correlation coefficient between theerror signal outputted by the AEC and the far-end reference signal, thesquare of the effective magnitude of the correlation coefficient betweenthe error signal outputted by the AEC and the far-end reference signalis: according to an Equation:

${{{\hat{r}}_{ex}(t)}}^{2} = \left\{ \begin{matrix}{{{\hat{r}(t)},}\mspace{79mu}} & {{{if}\mspace{14mu}{\hat{r}(t)}} > {{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2}} \\{{{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2},} & {{{if}\mspace{14mu}{\hat{r}(t)}} \leq {{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2}}\end{matrix} \right.$obtaining the square of the effective magnitude of the correlationcoefficient between the signal outputted by the AEC and the far-endreference signal; wherein |{right arrow over (r)}_(ex)(t)|² is thesquare of the effective magnitude of the correlation coefficient betweenthe error signal outputted by the AEC and the far-end reference signal;{circumflex over (r)}(t) is the candidate value of the square of themagnitude of the correlation coefficient between the error signaloutputted by the AEC and the far-end reference signal; t is adigital-signal sample time index number.

Optionally, a manner of obtaining the effective estimation value of thecoupling factor according to the biased estimation value of the couplingfactor and the correction factor is: obtaining the effective estimationvalue of the coupling factor according to the Equation:

${{\hat{\beta}(t)} = \frac{{\hat{\beta}(t)}❘_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{ex}(t)}}^{2}}},$wherein {circumflex over (β)}(t)) is the effective estimation value ofthe coupling factor; {circumflex over (β)}(t)|_(Cross-correlation) isthe biased estimation value of the coupling factor based on across-correlation technique; |{circumflex over (r)}_(ex)(t)|² is thesquare of the effective magnitude of the correlation coefficient betweenthe error signal outputted by the AEC and the far-end reference signal;t is a digital-signal sample time index number.

Optionally, the second obtaining unit is configured to: according to anEquation:

${{\delta^{opt}(t)} = {{\max\left\{ {\frac{L \cdot {{\hat{\sigma}}_{y}^{2}(t)}}{{\hat{\beta}(t)} + \rho_{0}},\delta_{\min}} \right\}} = {\max\left\{ {\frac{{L \cdot {{\hat{\sigma}}_{y}^{2}(t)}}{{{\hat{r}}_{ex}(t)}}^{2}}{{\hat{\beta}(t)}❘_{{Cross}\text{-}{correlation}}{+ \rho}},\delta_{\min}} \right\}}}},$obtain the time-varying regularization factor used for iterativelyupdating the coefficient vector of the FIR filter; wherein δ^(opt)(t) isthe time-varying regularization factor; L is a quantity of coefficientsof the FIR filter; {circumflex over (σ)}_(y) ²(t) is the power of thesignal received by the microphone;

${\hat{\beta}(t)} = \frac{{\hat{\beta}(t)}❘_{{Cross}\mspace{14mu}{correlation}}}{{{{\hat{r}}_{ex}(t)}}^{2}}$is the effective estimation value of coupling factor; {circumflex over(β)}(t)|_(Cross-correlation) is a biased estimation value of thecoupling factor based on a cross-correlation technique; |{circumflexover (r)}_(ex)(t)|² is a square of an effective magnitude of acorrelation coefficient between an error signal outputted by the AEC andthe far-end reference signal; δ_(min) is a preset small real constantquantity, and δ_(min)>0; ρ₀ and ρ are preset small real constants, andρ>0, ρ₀>0, respectively; t is a digital-signal sample time index number.

Optionally, the updating module is configured to: according to anEquation: {right arrow over (w)}(t+1)={right arrow over (w)}(t)+μ·{rightarrow over (x)}(t)e*(t)/[{right arrow over (x)}^(H)(t){right arrow over(x)}(t)+δ^(opt)(t)], sustainably adaptively update the coefficientvector of the FIR filter by applying a Normalized Least Mean Square(NLMS) algorithm, wherein, {right arrow over (w)}(t+1) is thecoefficient vector of the FIR filter after the coefficient vector of theFIR filter is updated; {right arrow over (w)}(t) is the coefficientvector of the FIR filter before the coefficient vector of the FIR filteris updated; μ is a predetermined coefficient updating step-sizeparameter, and 0<μ<2; {right arrow over (x)}(t) is a far-end referencesignal vector and {right arrow over (x)}(t)=[x(t), x(t−1), . . . ,x(t−L+1)]^(T); x(t−t2) is the far-end reference signal at a signalsample time (t−t2); T is a transpose operator; {right arrow over(x)}^(H)(t) is a conjugate transpose matrix of {right arrow over(x)}(t); δ^(opt)(t) is the time-varying regularization factor; e*(t) isa complex conjugate of e(t); e(t)=y(t)−{right arrow over(x)}^(H)(t){right arrow over (w)}(t), e(t) is the error signal outputtedby the AEC at a signal sample time t; y(t) is the signal received by themicrophone at the signal sample time t; {right arrow over(w)}(t)=[w₀(t), w₁(t), . . . , w_(L-1)(t)]^(T), w_(t2)(t) is a (t2+1)-thcoefficient of the FIR filter at the signal sample time t, t2=0, 1, 2, .. . , L−1; t is a digital-signal sample time index number.

Optionally, the updating module is configured to: according to anEquation: {right arrow over (w)}(t+1)={right arrow over(w)}(t)+μ·X_(state)(t)[X_(state)^(H)(t)X_(state)(t)+δ^(opt)(t)·I_(P×P)]⁻¹·{right arrow over (e)}*(t),applying an affine projection (AP) algorithm to sustainably adaptivelyupdate the coefficient vector of the FIR filter; wherein {right arrowover (w)}(t+1) is the coefficient vector of the FIR filter after thecoefficient vector of the FIR filter is updated; {right arrow over(w)}(t) is the coefficient vector of the FIR filter before thecoefficient vector of the FIR filter is updated; μ is a predeterminedcoefficient updating step-size parameter, and 0<μ<2; δ^(opt)(t) is thetime-varying regularization factor; X_(state)(t) is L×P-dimension statematrix, and X_(state)(t)=[{right arrow over (x)}(t), {right arrow over(x)}(t−1), . . . , {right arrow over (x)}(t−P+1)]; {right arrow over(x)}(t−t3) is a far-end reference signal vector at a signal sample time(t−t3), and t3=0, 1, . . . , P−1, P is an order quantity of the APalgorithm; X_(state) ^(H)(t) is a conjugate transpose matrix ofX_(state)(t); I_(P×P) is a P×P-dimension unit matrix; {right arrow over(e)}*(t) is a complex conjugate of {right arrow over (e)}(t), and {rightarrow over (e)}(t)={right arrow over (y)}(t)−X_(state) ^(H)(t){rightarrow over (w)}(t); {right arrow over (e)}(t) is a P-dimension errorvector; {right arrow over (y)}(t) is a P-dimension vector of the signalreceived by the microphone, and {right arrow over (y)}(t)=[y(t), y(t−1),. . . , y(t−P+1)]^(T); y(t−t3) is the signal received by the microphoneat a signal sample time (t−t3); {right arrow over (w)}(t)=[w₀(t), w₁(t),. . . , w_(L-1)(t)]^(T), w_(t2)(t) is a (t2+1)-th coefficient of the FIRfilter at a signal sample time t, t2=0, 1, 2, . . . , L−1; t is adigital-signal sample time index number.

Optionally, the preset signal includes a subband spectrum of a near-endspeech signal received by a microphone and inputted in an Acoustic EchoCanceller (AEC) and a subband spectrum of a far-end reference speechsignal; the coefficient vector of the FIR filter is a subband-domaincoefficient vector of the FIR filter and the time-varying regularizationfactor is a subband-domain time-varying regularization factor. Theobtaining module includes: a third obtaining unit, configured to obtaina subband power spectrum of the signal received by the microphone and aneffective estimation value of a subband-domain coupling factor,respectively; a fourth obtaining unit, configured to, according to thesubband power spectrum of the signal received by the microphone and theeffective estimation value of the subband-domain coupling factor, obtaina subband-domain time-varying regularization factor used for iterativelyupdating the subband-domain coefficient vector of the FIR filter in acase that the subband-domain coefficient vector of the FIR filter isused for processing a preset signal.

Optionally, a manner of obtaining the subband power spectrum of thesignal received by the microphone performed by the third obtaining unitis: according to an Equation:

${{\hat{\sigma}}_{Y}^{2}\left( {k,n} \right)} = \left\{ {\begin{matrix}{{{\alpha_{a} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}} + {\left( {1 - \alpha_{a}} \right) \cdot {{Y\left( {k,n} \right)}}^{2}}},{{{if}\mspace{14mu}{{Y\left( {k,n} \right)}}^{2}} > {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}}} \\{{{\alpha_{d} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}} + {\left( {1 - \alpha_{d}} \right) \cdot {{Y\left( {k,n} \right)}}^{2}}},{{{if}\mspace{14mu}{{Y\left( {k,n} \right)}}^{2}} \leq {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}}}\end{matrix},} \right.$obtaining the subband power spectrum of the signal received by themicrophone; wherein {circumflex over (σ)}_(Y) ²(k, n) is the subbandpower spectrum of the signal received by the microphone; Y(k, n) is asubband spectrum of the signal received by the microphone; α_(a) andα_(d) are preset recursive constant quantities, and 0≤α_(a)<α_(d)<1; kis a subband index variable, k=0, 1, 2, . . . , K−1, and K is a totalquantity of subbands; n is a signal frame time index variable.

Optionally, a manner of obtaining the effective estimation value of thesubband-domain coupling factor performed by the third obtaining unit,includes: obtaining a biased estimation value of the subband-domaincoupling factor according to a cross-correlation method; obtaining acorrection factor used for compensating for the biased estimation valueof the subband-domain coupling factor; obtaining the effectiveestimation value of the subband-domain coupling factor according to thebiased estimation value of the subband-domain coupling factor and thecorrection factor.

Optionally, a manner of obtaining the biased estimation value of thesubband-domain coupling factor according to the cross-correlation methodis: according to an Equation:

${{{\hat{\beta}\left( {k,n} \right)}❘_{{Cross}\text{-}{correlation}}} = \frac{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}\;{{E\left( {k,{n - {n\; 1}}} \right)}{X^{*}\left( {k,{n - {n\; 1}}} \right)}}}}^{2}}{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}\;{{X\left( {k,{n - {n\; 1}}} \right)}}^{2}}}^{2}}},$obtaining the biased estimation value of the subband-domain couplingfactor; wherein {circumflex over (β)}(k, n)|_(Cross-correlation) is thebiased estimation value of the subband-domain coupling factor; X*(k,n−n1) is a complex conjugate of X(k, n−n1); n1=0, 1, 2, . . . , N_(s)−1,N_(s) is a quantity of signal frames used in estimation of {circumflexover (β)}(k, n)|_(Cross-correlation), and N_(s)<<L_(s), L_(s) is aquantity of coefficients of the FIR filter in each subband; E(k, n−n1)is a subband spectrum of an error signal outputted by the AEC at asignal frame time (n−n1); E(k, n)=Y(k, n)−{right arrow over (X)}_(k)^(H)(n){right arrow over (W)}_(k)(n), E(k, n) is the subband spectrum ofthe error signal outputted by the AEC at a signal frame time n; Y(k, n)is the subband spectrum of the signal received by the microphone at thesignal frame time n; {right arrow over (X)}_(k) ^(H)(n) is a conjugatetranspose matrix of {right arrow over (X)}_(k)(n); {right arrow over(X)}_(k)(n) is a subband-spectrum vector of the far-end referencesignal, and {right arrow over (X)}_(k)(n)=[X(k, n), X(k, n−1), . . . ,X(k, n−L_(s)+1)]^(T); X(k, n−n2) is a subband spectrum of a far-endreference signal at a signal frame time (n−n2); T is a transposeoperator; {right arrow over (W)}_(k)(n) is the coefficient vector of theFIR filter in a subband k, {right arrow over (W)}_(k)(n)=[W₀(k, n),W₁(k, n), . . . , W_(L) _(s) ₋₁(k, n)]^(T); W_(n2)(k, n) is a (n2+1)-thcoefficient of the FIR filter in the subband k at the signal frame timen, n2=0, 1, 2, . . . , Ls−1; k is a subband index variable, k=0, 1, 2, .. . , K−1, and K is a total quantity of subbands; n is a signal frametime index variable.

Optionally, a manner of obtaining the correction factor used forcompensating for the biased estimation value of the subband-domaincoupling factor is: obtaining a candidate value of a square of amagnitude of a correlation coefficient between a subband spectrum of anerror signal outputted by an AEC and a subband spectrum of a far-endreference signal; obtaining, according to the candidate value of thesquare of the magnitude of the correlation coefficient between thesubband spectrum of the error signal outputted by the AEC and thesubband spectrum of the far-end reference signal, a square of aneffective magnitude of the correlation coefficient between the subbandspectrum of the error signal outputted by the AEC and the subbandspectrum of the far-end reference signal, and taking the square of theeffective magnitude of the correlation coefficient between the subbandspectrum of the error signal outputted by the AEC and the subbandspectrum of the far-end reference signal as the correction factor usedfor compensating for the biased estimation value of the subband-domaincoupling factor.

Optionally, a manner of obtaining the candidate value of the square ofthe magnitude of the correlation coefficient between the subbandspectrum of the error signal outputted by the AEC and the subbandspectrum of the far-end reference signal is: according to an Equation:

${{\hat{r}\left( {k,n} \right)} = \frac{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}\;{{E\left( {k,{n - {n\; 1}}} \right)}{X^{*}\left( {k,{n - {n\; 1}}} \right)}}}}^{2}}{\left( {\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}\;{{E\left( {k,{n - {n\; 1}}} \right)}}^{2}} \right)\left( {\sum\limits_{m = 0}^{N_{s} - 1}\;{{X\left( {k,{n - {n\; 1}}} \right)}}^{2}} \right)}},$obtaining the candidate value of the square of the magnitude of thecorrelation coefficient between the subband spectrum of the error signaloutputted by the AEC and the subband spectrum of the far-end referencesignal; wherein {right arrow over (r)}(k, n) is the candidate value ofthe square of the magnitude of the correlation coefficient between thesubband spectrum of the error signal outputted by the AEC and thesubband spectrum of the far-end reference signal; X*(k, n−n1) is acomplex conjugate of X(k, n−n1); n1=0, 1, 2, . . . , N_(s)−1, N_(s) is aquantity of frames used in estimation of {circumflex over (r)}(k, n),and N_(s)<<L_(s), L_(s) is a quantity of coefficients of the FIR filterin each subband; E(k, n−n1) is a subband spectrum of the error signaloutputted by the AEC at a signal frame time (n−n1); E(k, n)=Y(k,n)−{right arrow over (X)}_(k) ^(H)(n){right arrow over (W)}_(k)(n), E(k,n) is the subband spectrum of the error signal outputted by the AEC at asignal frame time n; Y(k, n) is a subband spectrum of a signal receivedby the microphone; {right arrow over (X)}_(k) ^(H)(n) is a conjugatetranspose matrix of {right arrow over (X)}_(k)(n); {right arrow over(X)}_(k)(n) is a subband spectrum vector of the far-end referencesignal, and {right arrow over (X)}_(k)(n)=[X(k, n), X(k, n−1), . . . ,X(k, n−L_(s)+1)]^(T); X(k, n−n2) is the subband spectrum of the far-endreference signal at the signal frame time (n−n2); T is a transposeoperator; {right arrow over (W)}_(k)(n) is the coefficient vector of theFIR filter in a subband k; {right arrow over (W)}_(k)(n)=[W₀(k, n),W₁(k, n), . . . , W_(L) _(s) ₋₁(k, n)]^(T); W_(n2)(k, n) is a (n2+1)-thcoefficient of the FIR filter in the signal frame time n in the subbandk; n2=0, 1, 2, . . . , Ls−1; k is a subband index variable; k=, 1, 2, .. . , K−1, and K is a total quantity of subbands; n is a signal frametime index variable.

Optionally, a manner of obtaining, according to the candidate value ofthe square of the magnitude of the correlation coefficient between thesubband spectrum of the error signal outputted by the AEC and thesubband spectrum of the far-end reference signal, the square of theeffective magnitude of the correlation coefficient between the subbandspectrum of the error signal outputted by the AEC and the subbandspectrum of the far-end reference signal is: according to an Equation:

${{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2} = \left\{ {\begin{matrix}{{{\hat{r}\left( {k,n} \right)},}} & {{{if}\mspace{14mu}{\hat{r}\left( {k,n} \right)}} > {{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2}} \\{{{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2},} & {{{if}\mspace{14mu}{\hat{r}\left( {k,n} \right)}} \leq {{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2}}\end{matrix},} \right.$obtaining the square of the effective magnitude of the correlationcoefficient between the subband spectrum of the error signal outputtedby the AEC and the subband spectrum of the far-end reference signal;wherein |{right arrow over (r)}_(EX)(k, n)|² is the square of theeffective magnitude of the correlation coefficient between the subbandspectrum of the error signal outputted by the AEC and the subbandspectrum of the far-end reference signal; {circumflex over (r)}(k, n) isthe candidate value of the square of the magnitude of the correlationcoefficient between the subband spectrum of the error signal outputtedby the AEC and the subband spectrum of the far-end reference signal; nis a signal frame time index variable.

Optionally, a manner of obtaining the effective estimation value of thesubband-domain coupling factor according to the biased estimation valueof the subband-domain coupling factor and the correction factor is:according to an Equation:

${{\hat{\beta}\left( {k,n} \right)} = \frac{{\hat{\beta}\left( {k,n} \right)}❘_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2}}},$obtaining the effective estimation value of the subband-domain couplingfactor; wherein {circumflex over (β)}(k, n) is the effective estimationvalue of the subband-domain coupling factor, {circumflex over (β)}(k,n)|_(Cross-correlation) is the biased estimation value of thesubband-domain coupling factor; |{circumflex over (r)}_(EX)(k, n)|² isthe square of the effective magnitude of the correlation coefficientbetween the subband spectrum of the error signal outputted by the AECand the subband spectrum of the far-end reference signal; n is a signalframe time index variable.

Optionally, the fourth obtaining unit is configured to: according to anEquation:

${\delta^{opt}\left( {k,n} \right)} = {{\max\left\{ {\frac{L_{s} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,n} \right)}}{{\hat{\beta}\left( {k,n} \right)} + \rho_{0}},\delta_{\min}} \right\}} = {\max\left\{ {\frac{{L_{s} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,n} \right)}}{{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2}}{{\hat{\beta}\left( {k,n} \right)}❘_{{Cross}\text{-}{correlation}}{+ \rho}},} \right.}}$δ_(min)}, obtain the subband-domain time-varying regularization factorused for iteratively updating the subband-domain coefficient vector ofthe FIR filter in a case that the subband-domain coefficient vector ofthe FIR filter is used for processing the preset signal; whereinδ^(opt)(k, n) is the subband-domain time-varying regularization factor;{circumflex over (σ)}_(Y) ²(k, n) is a subband power spectrum of asignal received by the microphone;

${\hat{\beta}\left( {k,n} \right)} = \frac{{\hat{\beta}\left( {k,n} \right)}❘_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2}}$is the effective estimation value of the subband-domain coupling factor,{circumflex over (β)}(k, n)|_(Cross-correlation) is the biasedestimation value of the subband-domain coupling factor; |{circumflexover (r)}_(EX)(k, n)|² is the square of the effective magnitude of thecorrelation coefficient between the subband spectrum of the error signaloutputted by the AEC and the subband spectrum of the far-end referencesignal; δ_(min) is a preset small real constant quantity, and δ_(min)>0;ρ₀ and ρ are preset small real constants, and ρ>0, ρ₀>0; n is a signalframe time index variable.

Optionally, the updating module is configured to: according to anEquation: {right arrow over (W)}_(k)(n+1)={right arrow over(W)}_(k)(n)+μ·{right arrow over (X)}_(k)(n)E*(k, n)/[{right arrow over(X)}_(k) ^(H)(n){right arrow over (X)}_(k)(n)+δ^(opt)(k, n)],sustainably adaptively update the subband-domain coefficient vector ofthe FIR filter using a Normalized Least Mean Square (NLMS) algorithm;wherein {right arrow over (W)}_(k)(n+1) is a coefficient vector of theFIR filter in a subband k after the coefficient vector of the FIR filteris updated; {right arrow over (W)}_(k)(n) is the coefficient vector ofthe FIR filter in the subband k before the coefficient vector of the FIRfilter is updated; μ is a predetermined coefficient updating step-sizeparameter, and 0<μ<2; {right arrow over (X)}_(k)(n) is a subbandspectrum vector of the far-end reference signal; {right arrow over(X)}_(k)(n)=[X(k, n), X(k, n−1), . . . , X(k, n−L_(s)+1)]^(T); X(k,n−n2) is a subband spectrum of the far-end reference signal at a signalframe time (n−n2); n2=0, 1, . . . , Ls−1, Ls is a quantity ofcoefficients of the FIR filter in each subband, T is a transposeoperator; {right arrow over (X)}_(k) ^(H)(n) is a conjugate transposematrix of {right arrow over (X)}_(k)(n); E*(k, n) is a complex conjugateof E(k, n); E(k, n) is the subband spectrum of the error signaloutputted by AEC at a signal frame time n, and E(k, n)=Y(k, n)−{rightarrow over (X)}_(k) ^(H)(n){right arrow over (W)}_(k)(n); Y(k, n) is thesubband spectrum of the signal received by the microphone at the signalframe time n; {right arrow over (W)}_(k)(n)=[W₀(k, n), W₁(k, n), . . . ,W_(L) _(s) ₋₁(k, n)]^(T); W_(n2)(k, n) is a (n2+1)-th coefficient of theFIR filter in the subband k at the signal frame time n; δ^(opt)(k, n) isthe subband-domain time-varying regularization factor; k is a subbandindex variable, k=0, 1, 2, . . . , K−1, and K is a total quantity ofsubbands; n is a signal frame time index variable.

Optionally, the updating module is configured to: according to anEquation: {right arrow over (W)}_(k)(n+1)={right arrow over(W)}_(k)(n)+μ·X_(state)(k, n)·[X_(state) ^(H)(k, n)X_(state)(k,n)+δ^(opt)(k, n)·I_(P×P)]⁻¹·{right arrow over (E)}_(k)*(n), substainablyadaptively update the subband-domain coefficient vector of the FIRfilter using an affine projection (AP) algorithm; wherein {right arrowover (W)}_(k)(n+1) is the coefficient vector of the FIR filter in asubband k after the coefficient vector of the FIR filter is updated;{right arrow over (W)}_(k)(n) is the coefficient vector of the FIRfilter in the subband k before the coefficient vector of the FIR filteris updated; μ is a predetermined coefficient updating step-sizeparameter, and 0<μ<2; δ^(opt)(k, n) is the subband-domain time-varyingregularization factor; X_(state)(k, n) is an L×P-dimension state matrixin the subband k, and X_(state)(k, n)=[{right arrow over (X)}_(k)(n),k(n−1), . . . , {right arrow over (X)}_(k)(n−P+1)]; {right arrow over(X)}_(k)(n−n3) is a subband spectrum vector of the far-end referencesignal at a signal frame time (n−n3), and n3=0, 1, . . . , P−1, P is anorder quantity of the AP algorithm; X_(state) ^(H)(k, n) is a conjugatetranspose matrix of X_(state)(k, n); I_(P×P) is a P×P-dimension unitmatrix; {right arrow over (E)}_(k)*(n) is a complex conjugate of {rightarrow over (E)}_(k)(n), and {right arrow over (E)}_(k)(n)={right arrowover (Y)}_(k)(n)−X_(state) ^(H)(k, n){right arrow over (W)}_(k)(n);{right arrow over (E)}_(k)(n) is a P-dimension subband spectrum vectorof an error signal; {right arrow over (Y)}_(k)(n) is a P-dimensionsubband spectrum vector of a signal received by the microphone, and{right arrow over (Y)}_(k)(n)=[Y(k, n), Y(k, n−1), . . . , Y(k,n−P+1)]^(T); Y(k, n−n3) is the signal received by the microphone at asignal frame time (n−n3); {right arrow over (W)}_(k)(n)=[W₀(k, n), W₁(k,n), . . . , W_(L) _(s) ₋₁(k, n)]^(T); W_(n2)(k, n) is a (n2+1)-thcoefficient of the FIR filter in the subband k at a signal frame time n,n2=0, 1, . . . , Ls−1, Ls is a quantity of coefficients of the FIRfilter in each subband; k is a subband index variable, k=0, 1, 2, . . ., K−1, and K is a total quantity of subbands; n is a signal frame timeindex variable.

Beneficial effects of the present disclosure are as follow: in the abovesolutions, the time-varying regularization factor used for iterativelyupdating the coefficient vector of the FIR filter is obtained, and thecoefficient vector of the FIR filter is sustainably adaptively updatedaccording to the time-varying regularization factor, thus, theperformance stability of the FIR filter is guaranteed, and the signalprocessing reliability is improved.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic diagram of an operation principle of an AEC;

FIG. 2 represents a flow diagram of a method of sustainably adaptivelyupdating a coefficient vector of an FIR filter according to someembodiments of the present disclosure;

FIG. 3 is a schematic diagram showing a structure of a time-domain LAEC;

FIG. 4 is a schematic diagram showing a structure of a subband domainAEC;

FIG. 5 shows a schematic view showing an LAEC structure of a subband k;

FIG. 6 is a schematic diagram showing a structure of a device ofsustainably adaptively updating a coefficient vector of an FIR filteraccording to some embodiments of the present disclosure; and

FIG. 7 illustrates a schematic diagram showing blocks in a device ofsustainably adaptively updating of a coefficient vector of a FIR filteraccording to some embodiments of the present disclosure.

DETAILED DESCRIPTION

In order to make objectives, technical solutions and advantages of thepresent disclosure clearer, the present disclosure will be described indetail below with reference to accompanying drawings and specificembodiments.

An operation principle of an AEC is shown in FIG. 1. FIG. 1 generallyincludes three processing modules, i.e., a linear echo canceller (LAEC),a “double talk” detector (DTD) and a nonlinear residual echo suppressor(RES). The LAEC estimates an echo signal (represented by d(t)) using anFIR linear filter and a downlink signal (i.e., a reference signal,represented by x(t)). The estimate (represented by {circumflex over(d)}(t)) is then subtracted from a signal (represented by y(t)) receivedby the microphone. The coefficient of the FIR filter is typicallyupdated adaptively using an echo cancellation (NLMS) algorithm by usingan error signal (represented by e(t)) only in a case of a “single talk”(i.e. no near-end speech signal (represented by s(t))), and is expressedby an Equation 1 as:e(t)=y(t)−{right arrow over (x)} ^(T)(t){right arrow over(w)}(t)  Equation 1:

Where,

$\begin{matrix}{{\overset{\rightarrow}{w}\left( {t + 1} \right)} = {{\overset{\rightarrow}{w}(t)} + {\mu\frac{{e(t)}{\overset{\rightarrow}{x}(t)}}{{{{\overset{\rightarrow}{x}}^{T}(t)}{\overset{\rightarrow}{x}(t)}} + \delta}}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

Where {right arrow over (w)}(t) denotes a coefficient vector of the FIRfilter at time t, and {right arrow over (w)}(t)=[w₀(t), . . . ,w_(L-1)(t)]^(T); {right arrow over (x)}(t) denotes a time delay linevector of a reference signal at the time t, and {right arrow over(x)}(t)=[x(t), . . . , x(t−L+1)]^(T); μ is a parameter of a constantlearning rate; δ is a regularization factor parameter of a small normalquantity; L is the number of filter coefficients.

In a case of a “double talk” (i.e. a near-end speech signal (s(t))exists), a coefficient updating process of the FIR filter will be frozento avoid divergence of a NLMS learning algorithm. A detection to the“double talk” is done by the DTD. Due to non-linearity of a downlinkspeaker and an uplink microphone, the LAEC can only cancel a linearcomponent of an echo signal in the signal (y(t)) received by themicrophone, and residual non-linear component of the echo signal will besuppressed by a subsequent residual echo suppression (RES) module.

It is pointed out above that an adaptive updating of an FIR filtercoefficient in the AEC must be performed only under the condition thatthere is no near-end speech signal (i.e., the “single talk” mode). In acase that a near-end speech signal exists (i.e., in a “double talk”mode), adaptive updating of a coefficient of the FIR filter must stoppedso as not to cause damage to the near-end speech signal due todivergence of the coefficient the filter. The detection in the “doubletalk” mode is usually done by the DTD, but a processing delay of the DTDand possible misjudgment thereof will seriously affect an adaptivelearning behavior of an AEC filter and further affect a performance ofthe AEC. For this reason, a number of adaptive variable step-sizelearning techniques are proposed to sustainably update the coefficientof the FIR filter iteratively, all of the techniques use a learning-ratevariable quantity (i.e. a variable step-size parameter), Thelearning-rate variable quantity is adaptively adjusted with respect to agradient of the learning-rate variable quantity according to a costfunction of the AEC, with a disadvantage that the learning-rate variablequantity is very sensitive to an initialization of an algorithm-relatedparameter and is only suitable for a stationary input signal. It is thusdifficult to obtain good performance in practical applications,especially in applications of echo cancellation. Thus, a so-calledgeneralized normalized gradient descent (GNGD) algorithm is proposed toovercome the above disadvantage, as shown in Equations 3 to 5:

$\begin{matrix}{{{\overset{\rightarrow}{w}\left( {t + 1} \right)} = {{{\overset{\rightarrow}{w}(t)} + {\mu\frac{{e(t)}{\overset{\rightarrow}{x}(t)}}{{{{\overset{\rightarrow}{x}}^{T}(t)}{\overset{\rightarrow}{x}(t)}} + {\delta(t)}}}} = {{\overset{\rightarrow}{w}(t)} + {{\eta(t)}{e(t)}{\overset{\rightarrow}{x}(t)}}}}};} & {{Equation}\mspace{14mu} 3} \\{\mspace{76mu}{{{\eta(t)} = \frac{\mu}{{{{\overset{\rightarrow}{x}}^{T}(t)}{\overset{\rightarrow}{x}(t)}} + {\delta(t)}}};}} & {{Equation}\mspace{14mu} 4} \\{\mspace{76mu}{{{\delta(t)} = {{\delta\left( {t - 1} \right)} - {\rho_{1}\mu\frac{{e(t)}{{e\left( {t - 1} \right)}\left\lbrack {{{\overset{\rightarrow}{x}}^{T}(t)}{\overset{\rightarrow}{x}\left( {t - 1} \right)}} \right\rbrack}}{\left\lbrack {{{{\overset{\rightarrow}{x}}^{T}\left( {t - 1} \right)}{\overset{\rightarrow}{x}\left( {t - 1} \right)}} + {\delta\left( {t - 1} \right)}} \right\rbrack^{2}}}}};}} & {{Equation}\mspace{14mu} 5}\end{matrix}$

Where η(t) is a learning rate; δ(t) is a learning rate controlparameter; ρ₁ is an adaptive iteration step-size parameter of δ(t).

Although the GNGD algorithm is insensitive to the initialization ofassociated parameters, the control parameter (δ(t)) of the learning rate(η(t)) is adaptively adjusted based on behavior characteristics of anNLMS random gradient, the GNGD can only make relatively slow adaptiveadjustment (often requiring tens or hundreds of samples). For thisreason, the GNGD algorithm cannot deal well with the “double talk” casewith the near-end (non-stationary) speech.

In view of the above, the present disclosure is directed to the problemthat an adaptive learning characteristic of a related FIR filter cannotguarantee performance stability of the FIR filter and affects a signalprocessing reliability, and the present disclosure provides a method anda device of sustainably updating a coefficient vector of a finiteimpulse response filter.

As shown in FIG. 2, some embodiments of the present disclosure provide amethod of sustainably adaptive updating a coefficient vector of a finiteimpulse response (FIR) filter. The method includes steps 21-22.

Step 21: obtaining a time-varying regularization factor used foriteratively updating a coefficient vector of a FIR filter in a case thatthe coefficient vector of the FIR filter is used for processing a presetsignal.

It should be noted that the preset signal includes one of combined pairsof following: a far-end reference speech signal inputted in an acousticecho canceller (AEC) and a near-end speech signal received by amicrophone; a noise reference signal and a system input signal in anadaptive noise cancellation system; an interference reference signal anda system input signal in an adaptive interference cancellation system,and an excitation input signal and an unknown system output signal to beidentified in adaptive system identification.

Step 22: updating the coefficient vector of the FIR filter according tothe time-varying regularization factor.

In the above solution, the time-varying regularization factor used foriteratively updating the coefficient vector of the FIR filter isobtained, and the coefficient vector of the FIR filter is sustainablyadaptively updated according to the time-varying regularization factor.In this way, the performance stability of the FIR filter is guaranteed,and the signal processing stability is improved.

Taking as an example a case in which the far-end reference speech signalinputted in the AEC and the near-end speech signal received by themicrophone are processed, and starting from a time domain and a subbanddomain, respectively, the present disclosure will be described asfollows.

I. Time Domain

For a time-domain LAEC shown in FIG. 2, the present disclosure focuseson a sustainable adaptive learning issue that a good robustness isgenerated to a “double talk” and an echo path change, and a real-timeon-line calculation method of time-varying regularization factor for atime-domain adaptive learning of the LAEC is presented. The adaptiveiterative updating algorithm for coefficient vector of the FIR filter inthe LAEC formed by using the time-varying regularization factor has agood robustness on the “double talk” and the echo path change. First,taking as an example a case of a normalized least mean square (NLMS)algorithm in a time-domain LAEC adaptive learning, a method of solvingthe time-varying regularization factor on-line in real time is describedin detail. Then, the time-varying regularization factor is applied tothe sustainable adaptive learning of the LAEC, and an NLMS algorithm ofthe time-varying regularization factor and an affine projection (AP)algorithm of the time-varying regularization factor are presentedrespectively.

Referring to the time-domain LAEC shown in FIG. 2, assuming that a timedomain signal involved is a complex signal, the coefficient vector ofthe FIR filter (or a complex coefficient vector of the FIR filter) attime t is expressed by an Equation 6: {right arrow over (w)}(t)=[w₀(t),w₁(t), . . . , w_(L-1)(t)]^(T).

Where w_(t2)(t) is the (t2+1)-th coefficient of the FIR filter at asignal sample time t, t2=0, 1, 2, . . . , L−1; L is the number ofcoefficients of the FIR filter; T is a transpose operator; t is a timeindex number of a digital signal sample.

Then the NLMS learning algorithm for updating the coefficient of the FIRfilter can be expressed by an Equation 7 and an Equation 8 as follow:e(t)=y(t)−{right arrow over (x)} ^(H)(t){right arrow over (w)}(t);{right arrow over (w)}(t+1)={right arrow over (w)}(t)+μ·{right arrowover (x)}(t)e*(t)/[{right arrow over (x)} ^(H)(t){right arrow over(x)}(t)+δ];

where e(t) is an error signal outputted by the AEC at a signal sampletime t; y(t) is a signal received by the microphone at the signal sampletime t; {right arrow over (x)}^(H)(t) is a conjugate transpose matrix of{right arrow over (x)}(t); {right arrow over (x)}(t) is a far-endreference signal vector; and a symbol “*” represents a complex conjugateoperation; H is a complex conjugate transpose operator; 0<μ<2 is apredetermined step-size parameter for updating a coefficient, δ>0 is asmall positive real constant called a “regularization factor” to avoidthe denominator of the Equation 8 to be zero.

Specifically, {right arrow over (x)}(t) is expressed as {right arrowover (x)}(t)

[x(t), x(t−1), . . . , x(t−L+1)]^(T), using the Equation 9;

where x(t−t2) is the far-end reference signal at the signal sample timet-t2.

y(t) is defined by an Equation 10: y(t)=d(t)+s(t);

where d(t) is an echo signal at time t; s(t) is the near-end speechsignal at the time t.

Since an adaptive iterative step-size of the filter coefficients can becontrolled by a using “time-varying” regularization factor δ(t)parameter, this “time-varying” regularization factor parameter δ(t) mustbe able to be solved on-line in real time. In fact, assuming that anideal complex coefficient vector of a sub-FIR filter is {right arrowover (h)}(t), which is estimated as {right arrow over (w)}(t), then anadaptive vector of a filter system is defined using an Equation 11 as:{right arrow over (Δ)}(t)

{right arrow over (h)}(t)−{right arrow over (w)}(t)

[Δ₀(t),Δ₁(t), . . . ,Δ_(L-1)(t)]^(T)

Then the error e(t) outputted by the LAEC can be expressed by anEquation 12 as follows:e(t)=y(t)−{right arrow over (x)} ^(H)(t){right arrow over(w)}(t)=∈(t)+s(t)

where ∈(t)

{right arrow over (x)}^(H)(t){right arrow over (Δ)}(t) is defined as adistortion-free error of the LAEC or a systematic error of the filter(i.e. a residual echo).

Then, the δ(t) must be chosen such that the following variablequantities are minimized, i.e. an Equation 13:E{∥{right arrow over (Δ)}(t)∥₂ ² }=E{{right arrow over (Δ)}^(H)(t){right arrow over (Δ)}(t)}→min

Here, E{⋅} is a statistical average operator, and ∥⋅∥₂ is a 2-norm of acomplex vector. According to the Equation 8, substituting δ(t) for δresults in an Equation 14: {right arrow over (Δ)}(t+1)={right arrow over(Δ)}(t)−μ·{right arrow over (x)}(t)e*(t)/[{right arrow over(x)}^(H)(t){right arrow over (x)}(t)+δ(t)].

Further, an Equation 15 is obtained by applying the 2-norm of thecomplex vector to the Equation 14:

${{\overset{\rightarrow}{\Delta}\left( {t + 1} \right)}}_{2}^{2} = {{{{\overset{\rightarrow}{\Delta}(t)}}_{2}^{2} - \frac{\mu \cdot \left\{ {{2{{\epsilon(t)}}^{2}} + {{s(t)}{\epsilon^{*}(t)}} + {{s^{*}(t)}{\epsilon(t)}}} \right\}}{{{\overset{\rightarrow}{x}(t)}}_{2}^{2} + {\delta(n)}} + \frac{\mu_{2}{{{\overset{\rightarrow}{x}(t)}}_{2}^{2} \cdot {{e(t)}}^{2}}}{\left\lbrack {{{\overset{\rightarrow}{x}(t)}}_{2}^{2} + {\delta(t)}} \right\rbrack^{2}}} \approx {{{\overset{\rightarrow}{\Delta}(t)}}_{2}^{2} - \frac{\mu \cdot \left\{ {{2{{\epsilon(t)}}^{2}} + {{s(t)}{\epsilon^{*}(t)}} + {{s^{*}(t)}{\epsilon(t)}}} \right\}}{{L\;{\sigma_{x}^{2}(t)}} + {\delta(t)}} + \frac{\mu^{2}L\;{{\sigma_{x}^{2}(t)} \cdot {{e(t)}}^{2}}}{\left\lbrack {{L\;{\sigma_{x}^{2}(t)}} + {\delta(t)}} \right\rbrack^{2}}}}$

Where σ_(x) ²(t) is expressed as by using an Equation 16: σ_(x)²(t)=E{|x(t)|²}

Assuming that s(t) is not related with x(t), then applying a statisticalaverage on both sides of the Equation 15 may derive an Equation 17:

${E\left\{ {{\overset{\rightarrow}{\Delta}\left( {t + 1} \right)}}_{2}^{2} \right\}} = {{E\left\{ {{\overset{\rightarrow}{\Delta}(t)}}_{2}^{2} \right\}} - \frac{2{{\mu\sigma}_{\epsilon}^{2}(t)}}{{L\;{\sigma_{x}^{2}(t)}} + {\delta(t)}} + \frac{\mu^{2}L\;{{\sigma_{x}^{2}(t)} \cdot {\sigma_{e}^{2}(t)}}}{\left\lbrack {{L\;{\sigma_{x}^{2}(t)}} + {\delta(t)}} \right\rbrack^{2}}}$

Further, an Equation 18 and an Equation 19 are defined:σ_(∈) ²(t)

E{|∈(t)|²};  Equation 18:σ_(e) ²(t)

E{|e(t)|²}  Equation 19:

Further assuming that a system adaptive vector {right arrow over (Δ)}(t)for a t-th frame is unrelated with δ(t) and is related only with δ(t−1),δ(t−2), . . . , etc., then an Equation 20 is provided as follows:

${\frac{\partial}{\partial{\delta(t)}}E\left\{ {{\overset{\rightarrow}{\Delta}\left( {t + 1} \right)}}_{2}^{2} \right\}} \approx {\frac{2{{\mu\sigma}_{\epsilon}^{2}(t)}}{\left\lbrack {{L\;{\sigma_{x}^{2}(t)}} + {\delta(t)}} \right\rbrack^{2}} - \frac{2\mu^{2}L\;{{\sigma_{x}^{2}(t)} \cdot {\sigma_{e}^{2}(t)}}}{\left\lbrack {{L\;{\sigma_{x}^{2}(t)}} + {\delta(t)}} \right\rbrack^{3}}}$

If the Equation twenty is set as zero, then an optimal regularizationfactor δ^(opt)(t) is obtained and expressed by an Equation 21 as:

${\delta^{opt}(t)} = \frac{L \cdot {{\sigma_{x}^{2}(t)}\left\lbrack {{{\mu\sigma}_{e}^{2}(t)} - {\sigma_{\epsilon}^{2}(t)}} \right\rbrack}}{\sigma_{\epsilon}^{2}(t)}$

Assuming further that L>>1, and that x(t) is a whitened excitationsignal (if the x(t) is not the whitened excitation signal, then x(t) canbe changed to a whitened excitation source by a whitening process), thenthere are following Equations:σ_(∈) ²(t)=E{|∈(t)|²}=σ_(x) ²(t)E{∥{right arrow over (Δ)}(t)∥₂²}  Equation 22:σ_(e) ²(t)=σ_(∈) ²(t)+σ_(s) ²(t), where σ_(s) ²(t)

E{|s(t)|²}  Equation 23:

If μ=1, then the Equation 21 can be expressed by using the Equation 24as follows.

$\begin{matrix}{{\delta^{opt}(t)} = {\frac{L \cdot {{\sigma_{x}^{2}(t)}\left\lbrack {{{\mu\sigma}_{e}^{2}(t)} - {\sigma_{e}^{2}(t)} + {\sigma_{s}^{2}(t)}} \right\rbrack}}{{\sigma_{x}^{2}(t)}E\left\{ {{\overset{\rightarrow}{\Delta}(t)}}_{2}^{2} \right\}} = \frac{L\left\lbrack {{\sigma_{y}^{2}(t)} - {\sigma_{d}^{2}(t)}} \right\rbrack}{E\left\{ {{\overset{\rightarrow}{\Delta}(n)}}_{2}^{2} \right\}}}} & {{Equation}\mspace{14mu} 24}\end{matrix}$

In the Equation 24, σ_(y) ²(t)

E{|y(t)|²} is a power of a signal received by the microphone at time t,and σ_(d) ²(t)

E{|d(t)|²} is a power of an echo signal at the time t. Since the powerσ_(d) ²(t) of the echo signal is not directly available, the presentdisclosure simplifies, for purpose of facilitating actualimplementation, the Equation 24 to:

$\begin{matrix}{{\delta^{opt}(t)} \approx \frac{L \cdot {\sigma_{y}^{2}(t)}}{E\left\{ {{\overset{\rightarrow}{\Delta}(t)}}_{2}^{2} \right\}}} & {{Equation}\mspace{14mu} 25}\end{matrix}$

δ^(opt)(t) as a control variable quantity for the iteration step-size ofupdating a filter coefficient vector, can be estimated and obtained byusing the Equation 25. Although the adaptive iteration of the filtercoefficients is slightly over-suppressed in the presence of echoes, adesired effect of slowing down the iteration of updating a filtercoefficient vector can still be achieved when a near-end speech signalhaving a higher level is present. The engineering calculation of σ_(y)²(t) can be implemented on-line in real time by using a first orderrecursive model, considering that σ_(y) ²(t) must track a change of alevel of the near-end speech signal in time, the present disclosure usesthe following manner of “fast attack/slow decay” to estimate thevariable σ_(y) ²(t) online in real time, that is, an Equation 26 isprovided:

${{\hat{\sigma}}_{y}^{2}(t)} = \left\{ \begin{matrix}{{{\alpha_{a} \cdot {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}} + {\left( {1 - \alpha_{a}} \right) \cdot {{y(t)}}^{2}}},{{{if}\mspace{14mu}{{y(t)}}^{2}} > {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}}} \\{{{\alpha_{d} \cdot {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}} + {\left( {1 - \alpha_{d}} \right) \cdot {{y(t)}}^{2}}},{otherwise}}\end{matrix} \right.$

where 0≤α_(a)<α_(d)<1 is a preset recursive constant.

In addition, the Equation 25 also relates to estimation of the parameterE{∥{right arrow over (Δ)}(t)∥₂ ²}, the estimation of the parameterE{∥{right arrow over (Δ)}(t)∥₂ ²} is actually estimation of an echocoupling factor β(t). In the case of the “simple talk,” the couplingfactor. β(t), according to a definition thereof, can be estimateddirectly by the following Equation 27, i.e.:

$\left. {\beta(t)} \right|_{Direct}\overset{\Delta}{=}{\frac{E\left\{ {{\epsilon(t)}}^{2} \right\}}{E\left\{ {{x(t)}}^{2} \right\}}\underset{\mspace{14mu}{{Single}\mspace{14mu}{talk}}{\;\mspace{14mu}}}{\Longleftrightarrow}\frac{E\left\{ {{e(t)}}^{2} \right\}}{E\left\{ {{x(t)}}^{2} \right\}}}$

Obviously, the Equation 27 cannot be applied to the case of “doubletalk”. In the case of the “double talk”, in order to overcome aninfluence of the near-end speech s(t), we propose a cross-correlationtechnique to estimate the coupling factor β(t), that is, an Equation 28:

$\left. {\beta(t)} \right|_{{c{ross}}\text{-}{correlation}}\overset{\Delta}{=}{\frac{{{E\left\{ {{e(t)}{x^{*}(t)}} \right\}}}^{2}}{\left\lbrack {E\left\{ {{x(t)}}^{2} \right\}} \right\rbrack^{2}} = {\frac{{{E\left\{ {{\epsilon(t)}{x^{*}(t)}} \right\}}}^{2}}{\left\lbrack {E\left\{ {{x(t)}}^{2} \right\}} \right\rbrack^{2}}.}}$

Although the Equation 28 removes the influence of the near-end speechs(t) by using statistical non-correlation between the near-end signals(t) and the reference signal x(t), this estimate is a biased estimateand therefore there is a bias. Therefore, we need to compensate it toimprove an estimation accuracy of coupling factor β(t).

Considering now the case of the “single talk”, by dividing the Equation28 with the Equation 27, an Equation 28 may be obtained:

${\frac{\left. {\beta(t)} \right|_{Cr{oss}\text{-}{correlation}}}{\left. {\beta(t)} \right|_{Direct}} = {{\frac{{{E\left\{ {{\epsilon(t)}{x^{*}(t)}} \right\}}}^{2}}{E{\left\{ {{\epsilon(t)}}^{2} \right\} \cdot E}\left\{ {{x(t)}}^{2} \right\}}\underset{\mspace{14mu}{{Single}\mspace{14mu}{talk}}{\;\mspace{14mu}}}{\Longleftrightarrow}\frac{{{E\left\{ {{e(t)}{x^{*}(t)}} \right\}}}^{2}}{E{\left\{ {{e(t)}}^{2} \right\} \cdot E}\left\{ {{x(t)}}^{2} \right\}}} = {{{r_{ex}(t)}}^{2} \leq 1}}};$

where r_(ex)(t) is a (normalized) correlation coefficient between theLAEC output signal e (t) and the far-end reference signal x(n) in thecase of the “single talk”. The Equation 29 shows that, in the case of“single talk”, a square of a magnitude of the (normalized) correlationcoefficient between the time-domain LAEC output signal e(t) and thefar-end reference signal x(t) is exactly a ratio betweenβ(t)|_(Cross-correlation) estimated by the cross-correlation techniqueand β(t)|_(Direct) estimated by the direct method. If we can estimatethe square of the magnitude of this correlation coefficient effectively,we can use it to compensate and correct β(t)|_(Cross-correlation)estimated by the cross-correlation technique, thereby improving anestimation accuracy of the couple factor β(t). That is to say, theestimation of E{∥{right arrow over (Δ)}(t)∥₂ ²} can be expressed byusing an Equation 30 as:

$\left\{ {{\overset{\rightarrow}{\Delta}(t)}}_{2}^{2} \right\}\overset{\Delta}{=}{{\beta(t)} = {\frac{\left. {\beta(t)} \right|_{{cross}\text{-}{correlation}}}{{{r_{ex}(t)}}^{2}}.}}$

Obviously, the Equation 30 for estimating the coupling factor appliesnot only to the case of the “single talk” but also applies to the caseof “double talk” as long as the square of the magnitude of thecorrelation coefficient between e(t) and x(t) can be effectivelyestimated. How to correctly and effectively estimate the square of themagnitude of the correlation coefficient can be obtained from followinganalytical discussion and a relevant inspiration may be obtained. Takingnote of such a fact that in the case of “single talk”, |r_(ex)(t)|² maybe calculated according to the definition given in Equation 29, that isan Equation 31 is provided:

${{r_{ex}(t)}}^{2} = {\frac{{{E\left\{ {{e(t)}{x^{*}(t)}} \right\}}}^{2}}{E{\left\{ {{e(t)}}^{2} \right\} \cdot E}\left\{ {{x(t)}}^{2} \right\}}\underset{\mspace{14mu}{{Single}\mspace{14mu}{talk}}{\;\mspace{14mu}}}{\Longleftrightarrow}\frac{{{E\left\{ {{\epsilon(t)}{x^{*}(t)}} \right\}}}^{2}}{E\left\{ {{\epsilon(t)}}^{2} \right\} E\left\{ {{x(t)}}^{2} \right\}}}$

In the case of “double talk”, note that a candidate value of the squareof the magnitude of the correlation coefficient at this time is r(t),then r(t) can be express by using an Equation 32 as:

${r(t)} = {{\frac{{{E\left\{ {{e(t)}{x^{*}(t)}} \right\}}}^{2}}{E\left\{ {{e(t)}}^{2} \right\} E\left\{ {{x(t)}}^{2} \right\}}\underset{\mspace{14mu}{{Double}\mspace{14mu}{talk}}{\;\mspace{14mu}}}{\Longleftrightarrow}\frac{{{E\left\{ {{\epsilon(t)}{x(t)}} \right\}}}^{2}}{\left\lbrack {{E\left\{ {{\epsilon(t)}}^{2} \right\}} + {E\left\{ {{s(t)}}^{2} \right\}}} \right\rbrack E\left\{ {{x(t)}}^{2} \right\}}} < {{r_{ex}(t)}}^{2}}$

Considering comprehensively an effective estimation value of theEquation 31 and the Equation 32, the effective estimate |{circumflexover (r)}_(ex)(t)|² of |r_(ex)(t)|² can be expressed by using thefollowing Equation 33:

${{{\hat{r}}_{ex}(t)}}^{2} = \left\{ \begin{matrix}{{\hat{r}(t)},} & {{{If}\mspace{14mu}{\hat{r}(t)}} > {{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2}} \\{{{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2},} & {Otherwise}\end{matrix} \right.$

In terms of engineering implementation, the estimation of theβ(t)|_(Cross-correlation) in the Equation 28 can be approximatelyexpressed by using the Equation 34 as:

$\left. {\hat{\beta}(t)} \middle| {}_{C\;{ross}\text{-}{correlation}}{\approx {\frac{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{e\left( {t - {t\; 1}} \right)}{x^{*}\left( {t - {t\; 1}} \right)}}}}^{2}}{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{x\left( {t - {t\; 1}} \right)}}^{2}}}^{2}}.}} \right.$

The Equation 32 can be expressed by using an Equation 35 as:

${\hat{r}(t)} \approx {\frac{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{e\left( {t - {t\; 1}} \right)}{x^{*}\left( {t - {t\; 1}} \right)}}}}^{2}}{\left( {\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{e\left( {t - {t\; 1}} \right)}}^{2}} \right)\left( {\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{x\left( {t - {t\; 1}} \right)}}^{2}} \right)}.}$

Where T_(s)<<L is a positive integer which is used to estimate thenumber of samples used in Equations 34 and 35. The Equation 30, and theEquation 33 to the Equation 35 constitute a high-precision estimationalgorithm for β(t)

E{∥{right arrow over (Δ)}(t)∥₂ ²} that can be used for both the case of“double talk” and a case of an echo path changing scene. The estimatedvalue obtained by this algorithm is substituted into the Equation 25 toobtain an Equation 36:

${\delta^{opt}(t)} = {\frac{L \cdot {{\hat{\sigma}}_{y}^{2}(t)}}{{\overset{\hat{}}{\beta}(t)} + \rho_{0}} = \frac{{L \cdot {{\hat{\sigma}}_{y}^{2}(t)}}{{{\hat{r}}_{ex}(t)}}^{2}}{\left. {\overset{\hat{}}{\beta}(t)} \middle| {}_{{Cross}\text{-}{correlation}}{+ \rho} \right.}}$

where ρ and ρ₀>0 are very small real constants in order to avoid thedenominator of the Equation 36 from being zero,

${{\overset{\hat{}}{\beta}(t)} = \frac{\left. {\overset{\hat{}}{\beta}(t)} \right|_{{C{ross}}\text{-}{correlation}}}{\left| {{\overset{\hat{}}{r}}_{ex}(t)} \right|^{2}}}.$From the Equation 36, it can be seen that in the case of “single talk”,{circumflex over (σ)}_(y) ²(t)=σ_(d) ²(t), if the echo path changes atthis time, in the case, {circumflex over (σ)}_(∈) ²(t) will increaserapidly, leading to |{circumflex over (r)}_(ex)(t)|²≈1. Correspondingly,{circumflex over (β)}(t)|_(Cross-correlation) also increases rapidly(because the residual echo signal x(t) is strongly correlated with thefar-end reference signal x(t) at this time), while a change of{circumflex over (σ)}_(y) ²(t) is not too large. Therefore, δ^(opt)(t)becomes smaller, which is advantageous to accelerate the iteration ofupdating the coefficient vector of a subband FIR filter. In the case of“double talk”, {circumflex over (σ)}_(y) ²(t)=σ_(d) ²(t)+σ_(s) ²(t),σ_(s) ²(t) of the near-end speech signal appears, causing that{circumflex over (σ)}_(y) ²(t) rapidly increases. On the other hand, theestimation (see the Equations 33 and 34) of {circumflex over(β)}(t)|_(Cross-correlation) and the estimation of |{circumflex over(r)}_(ex)(t)|² are robust to the case of “double talk”, thus, parametervalues of the estimations do not change significantly. Therefore,δ^(opt)(t) becomes larger rapidly, which greatly slows down theiteration of updating the filter coefficient vector, and avoidsdivergence of updating the filter coefficient vector in the case of“double talk”.

On the other hand, under a condition that a level of the near-end speechsignal is low or there is no near-end speech signal, in order tostabilize the iterative algorithm for updating a filter coefficientvector, the regularization factor must be defined to be a small realconstant δ_(min)>0. Therefore, a reasonable on-line calculation Equationof the regularization factor is shown in an Equation 37:

${{\delta^{opt}(t)} = {\max\left\{ {\frac{{L \cdot {{\hat{\sigma}}_{y}^{2}(t)}}{{{\hat{r}}_{ex}(t)}}^{2}}{\left. {\overset{\hat{}}{\beta}(t)} \middle| {}_{{Cross}\text{-}{correlation}}{+ \rho} \right.},\delta_{\min}} \right\}}}\;$

From the above derivation process, it can be seen that a time-domainδ^(opt)(t) can be obtained by obtaining {circumflex over (σ)}_(y) ²(t),|{circumflex over (r)}_(ex)(t)|², {circumflex over(β)}(t)|_(Cross-correlation). The specific implementation of the step 21in the present disclosure is as follows: obtaining a power of a signalreceived by a microphone and an effective estimation value of a couplingfactor; according to the power of the signal received by the microphoneand the effective estimation value of the coupling factor, obtaining atime-varying regularization factor for iteratively updating thecoefficient vector of the FIR filter when the coefficient vector of theFIR filter is used for processing a preset signal.

Further, the method of obtaining the power of the signal received by themicrophone is as follows:

according to the Equation 26:

${{\hat{\sigma}}_{y}^{2}(t)} = \left\{ \begin{matrix}{{{\alpha_{a} \cdot {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}} + {\left( {1 - \alpha_{a}} \right) \cdot {{y(t)}}^{2}}},{{{if}\mspace{14mu}{{y(t)}}^{2}} > {{\hat{\sigma}}_{y}^{2}\ \left( {t - 1} \right)}}} \\{{{\alpha_{d} \cdot {{\hat{\sigma}}_{y}^{2}\ \left( {t - 1} \right)}} + {\left( {1 - \alpha_{d}} \right) \cdot {{y(t)}}^{2}}},{{{if}\mspace{14mu}{{y(t)}}^{2}}\  \leq {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}}}\end{matrix} \right.$

obtaining the power of the signal received by the microphone;

where {circumflex over (σ)}_(y) ²(t) is the power of the signal receivedby the microphone; y(t) is the signal received by the microphone; α_(a)and α_(d) are preset recursive constant quantities, and 0≤α_(a)<α_(d)<1;t is a digital-signal time index number.

Further, the method of obtaining the effective estimation value of thecoupling factor is as follows: obtaining a biased estimation value ofthe coupling factor according to a cross-correlation method; obtaining acorrection factor used for compensating for the biased estimation valueof the coupling factor; obtaining the effective estimation value of thecoupling factor according to the biased estimation value of the couplingfactor and the correction factor.

Further, the step of obtaining the biased estimation value of thecoupling factor according to the cross-correlation method includes:

according to the Equation 34:

${\left. {\overset{\hat{}}{\beta}(t)} \right|_{{C{ross}}\text{-}{correlation}} = \frac{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{e\left( {t - {t\; 1}} \right)}{x^{*}\left( {t - {t\; 1}} \right)}}}}^{2}}{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{x\left( {t - {t\; 1}} \right)}}^{2}}}^{2}}},$obtaining the biased estimation value of the coupling factor,

where {circumflex over (β)}(t)|_(Cross-correlation) is the biasedestimation value of the coupling factor based on the cross-correlationtechnique. x*(t−t1) is a complex conjugate of x(t−t1); t1=0, 1, 2, . . ., T_(s)−1, T_(s) is the number of samples used in the estimation of the{circumflex over (β)}(t)|_(Cross-correlation), and T_(s)<<L, L is thenumber of coefficients of the FIR filter; e(t−t1) is an error signaloutputted by the AEC at the signal sample time (t−t1), e(t)=y(t)−{rightarrow over (x)}^(H)(t){right arrow over (w)}(t), e(t) is an error signaloutputted by the AEC at the signal sample time t; y(t) is a signalreceived by the microphone at the signal sample time t; {right arrowover (x)}^(H)(t) is a conjugate transpose matrix of {right arrow over(x)}(t); {right arrow over (x)}(t) is the far-end reference signalvector and {right arrow over (x)}(t)=[x(t), x(t−1), . . . ,x(t−L+1)]^(T); x(t−t2) is the far-end reference signal at the signalsample time (t−t2); T is a transpose operator; {right arrow over (w)}(t)is the coefficient vector of the FIR filter, {right arrow over(w)}(t)=[w₀(t), w₁(t), . . . , w_(L-1)(t)]^(T), w_(t2)(t) is the(t2+1)-th coefficient of the FIR filter at the signal sample time t,t2=0, 1, 2, . . . , L−1.

Further, the step of obtaining the correction factor used forcompensating for the biased estimation value of the coupling factorincludes: obtaining a candidate value of the square of a magnitude of acorrelation coefficient between the error signal outputted by the AECand the far-end reference signal; obtaining the square of an effectivemagnitude of the correlation coefficient between the error signaloutputted by the AEC and the far-end reference signal based on thecandidate value of the square of the magnitude of the correlationcoefficient between the error signal outputted by the AEC and thefar-end reference signal, and taking the square of an effectivemagnitude of the correlation coefficient between the error signaloutputted by the AEC and the far-end reference signal as the correctionfactor used for compensating for the biased estimation value of thecoupling factor.

Further, the step of obtaining the candidate value of the square of themagnitude of the correlation coefficient between the error signaloutputted by the AEC and the far-end reference signal includes:according to the Equation 35:

${{\hat{r}(t)} = \frac{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{e\left( {t - {t\; 1}} \right)}{x^{*}\left( {t - {t\; 1}} \right)}}}}^{2}}{\left( {\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{e\left( {t - {t\; 1}} \right)}}^{2}} \right)\left( {\sum\limits_{t = 0}^{T_{s} - 1}{{x\left( {t - {t\; 1}} \right)}}^{2}} \right)}},$obtaining the candidate value of the square of the magnitude of thecorrelation coefficient between the error signal outputted by the AECand the far-end reference signal;

where {circumflex over (r)}(t) is the candidate value of the square ofthe magnitude of the correlation coefficient between the error signaloutputted by the AEC and the far-end reference signal; x(t−t1) is thefar-end reference signal at the signal sample time (t−t1); x*(t−t1) isthe complex conjugate of x(t−t1); t1=0, 1, 2, . . . , T_(s)−1, T_(s) isthe number of samples used in the estimation of {circumflex over(r)}(t), and T_(s)<<L, L is the number of coefficients of the FIRfilter; e(t−t1) is the error signal outputted by the AEC at the signalsample time (t−t1), e(t)=y(t)−{right arrow over (x)}^(H)(t){right arrowover (w)}(t), e(t) is an error signal outputted by the AEC at the signalsample time t; y(t) is the signal received by the microphone at thesignal sample time t; {right arrow over (x)}^(H)(t) is the conjugatetranspose matrix of {right arrow over (x)}(t); {right arrow over (x)}(t)is the far-end reference signal vector and {right arrow over(x)}(t)=[x(t), x(t−1), . . . , x(t−L+1)]^(T); x(t−t2) is the far-endreference signal at the signal sample time (t−t2); T is a transposeoperator; {right arrow over (w)}(t) is the coefficient vector of the FIRfilter, {right arrow over (w)}(t)=[w₀(t), w₁(t), . . . ,w_(L-1)(t)]^(T), w_(t2)(t) (t) is the (t2+1)-th coefficient of the FIRfilter at the signal sample time t, t2=0, 1, 2, . . . , L−1.

Further, the step of obtaining the square of the effective magnitude ofthe correlation coefficient between the error signal outputted by theAEC and the far-end reference signal according to the candidate value ofthe square of the magnitude of the correlation coefficient between theerror signal outputted by the AEC and the far-end reference signal,includes:

according to an Equation 33:

${{{\hat{r}}_{ex}(t)}}^{2} = \left\{ \begin{matrix}{{\hat{r}(t)},} & {{{if}\mspace{14mu}{\hat{r}(t)}} > {{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2}} \\{{{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2},} & {{{if}\mspace{14mu}{\hat{r}(t)}} \leq {{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2}}\end{matrix} \right.$

obtaining the square of the effective magnitude of the correlationcoefficient between the signal outputted by the AEC and the far-endreference signal;

where |{circumflex over (r)}_(ex)(t)|² is the square of the effectivemagnitude of the correlation coefficient between the error signaloutputted by the AEC and the far-end reference signal; {right arrow over(r)}(t) is the candidate value of the square of the magnitude of thecorrelation coefficient between the error signal outputted by the AECand the far-end reference signal.

Further, the step of obtaining the effective estimation value of thecoupling factor according to the biased estimation value of the couplingfactor and the correction factor includes: obtaining the effectiveestimation value of the coupling factor according to the Equation:

${{\hat{\beta}(t)} = \frac{\left. {\hat{\beta}(t)} \right|_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{ex}(t)}}^{2}}},$

where {circumflex over (β)}(t)) is the effective estimation value of thecoupling factor; {circumflex over (β)}(t)|_(Cross-correlation) is thebiased estimation value of the coupling factor based on thecross-correlation technique; |{circumflex over (r)}_(ex)(t)|² is thesquare of the effective magnitude of the correlation coefficient betweenthe error signal outputted by the AEC and the far-end reference signal.

Further, the step of obtaining the time-varying regularization factorused for iteratively updating the coefficient vector of the FIR filterwhen processing the preset signal using the coefficient vector of theFIR filter, according to the effective estimation values of the power ofthe signal received by the microphone and the coupling factor, includes:according to an Equation 36 and an Equation 37:

${{\delta^{opt}(t)} = {{\max\left\{ {\frac{L \cdot {{\hat{\sigma}}_{y}^{2}(t)}}{{\hat{\beta}(t)} + \rho_{0}},\delta_{\min}} \right\}} = {\max\left\{ {\frac{{L \cdot {{\hat{\sigma}}_{y}^{2}(t)}}{{{\hat{r}}_{ex}(t)}}^{2}}{\left. {\hat{\beta}(t)} \middle| {}_{{Cross}\text{-}{correlation}}{+ \rho} \right.},\delta_{\min}} \right\}}}},$obtaining the time-varying regularization factor used for iterativelyupdating the coefficient vector of the FIR filter;

where δ^(opt)(t) is the time-varying regularization factor; L is thenumber of coefficients of the FIR filter; {circumflex over (σ)}_(y) ²(t)is the power of the signal received by the microphone;

${\hat{\beta}(t)} = \frac{\left. {\hat{\beta}(t)} \right|_{{Cross}\mspace{14mu}{correlation}}}{{{{\hat{r}}_{ex}(t)}}^{2}}$is the effective estimation value of coupling factor; {circumflex over(β)}(t)|_(Cross-correlation) is the biased estimation value of thecoupling factor based on the cross-correlation technique; |{circumflexover (r)}_(ex)(t)|² is the square of the effective magnitude of thecorrelation coefficient between the error signal outputted by the AECand the far-end reference signal; δ_(min) is a preset small realconstant quantity, and δ_(min)>0; ρ₀ and ρ are preset small realconstants, and ρ>0, ρ₀>0, respectively.

Specifically, when sustainably adaptively updating the coefficientvector of the FIR filter using the NLMS algorithm, specificimplementation of the step 22 is as follows:

according to an Equation: {right arrow over (w)}(t+1)={right arrow over(w)}(t)+μ·{right arrow over (x)}(t)e*(t)/[{right arrow over(x)}^(H)(t){right arrow over (x)}(t)+δ^(opt)(t)], sustainably adaptivelyupdating the coefficient vector of the FIR filter by applying aNormalized Least Mean Square (NLMS) algorithm, wherein, {right arrowover (w)}(t+1) is the coefficient vector of the FIR filter after thecoefficient vector of the FIR filter is updated; {right arrow over(w)}(t) is the coefficient vector of the FIR filter before thecoefficient vector of the FIR filter is updated; μ is a predeterminedcoefficient updating step-size parameter, and 0<μ<2; {right arrow over(x)}(t) is the far-end reference signal vector and {right arrow over(x)}(t)=[x(t), x(t−1), . . . , x(t−L+1)]^(T); x(t−t2) is the far-endreference signal at the signal sample time (t−t2); T is a transposeoperator; {right arrow over (x)}^(H)(t) is the conjugate transposematrix of {right arrow over (x)}(t); δ^(opt)(t) is the time-varyingregularization factor; e*(t) is the complex conjugate of e(t);e(t)=y(t)−{right arrow over (x)}^(H)(t){right arrow over (w)}(t), e(t)is the error signal outputted by the AEC at the signal sample time t;y(t) is the signal received by the microphone at the signal sample timet; {right arrow over (w)}(t)=[w₀(t), w₁(t), . . . , w_(L-1)(t)]^(T),w_(t2)(t) is the (t2+1)-th coefficient of the FIR filter at the signalsample time t, t2=0, 1, 2, . . . , L−1.

The time-varying regularization factor δ^(opt)(t) defined by theEquation 37 is applied to the NLMS adaptive learning algorithm of thetime-domain LAEC, an NLMS sustainable adaptive learning algorithm withthe time-varying regularization factor in the time-domain LAEC can beobtained. The algorithm has good robustness to both the “double talk”and an echo path change. A specific algorithm implementation flow is asfollows:

Step 1: Initializing

1.1 Presetting parameters 0<μ<2, 0≤α_(a)<α_(d)<1, τ≥0, ρ>0, and valuesof δ_(min), L, and T_(s);

1.2 Initializing relevant variables

{right arrow over (x)}(0)=0, {right arrow over (h)}(o)=0, {circumflexover (σ)}_(y) ²(0)=0, |{circumflex over (r)}_(ex)(t)|²=τ;

1.3 Setting a signal time index variable “t” to zero, that is, t=0;

Step 2: Calculating a relevant variable online

2.1 Using the Equation 26 to calculate {circumflex over (σ)}_(y) ²(t);

2.2 Calculating the output e(t) of the LAEC using the Equation 7;

2.3 Calculating |{circumflex over (r)}_(ex)(t)|² using the Equation 33and the Equation 35;

2.4 Calculating {circumflex over (β)}(t)|_(Cross-correlation) using theEquation 34;

Step 3: Calculating the regularization factor δ^(opt)(t) online

3.1 Using the Equation 37 to calculate opt(t);

Step 4: Updating the Iterated Coefficient Vector of the FIR Filter

4.1 Calculating the coefficient vector of the FIR filter using theEquation {right arrow over (w)}(t+1)={right arrow over (w)}(t)+μ·{rightarrow over (x)}(t)e*(t)/[{right arrow over (x)}^(H)(t){right arrow over(x)}(t)+δ^(opt)(t)];

Step 5: Updating a signal frame index variable “t,” that is, t=t+1, andjumping to the Step 2.

It should be noted that an optimal time-varying regularization factordetermined by the Equation 37 is also applicable to an adaptive learningAP algorithm of the time-domain LAEC. In fact, the adaptive learning APalgorithm of the time-domain LAEC can be characterized by using theEquation 38 and the Equation 39 as follows: {right arrow over(e)}(t)={right arrow over (y)}(t)−X_(state) ^(H)(t){right arrow over(w)}(t);{right arrow over (w)}(t+1)={right arrow over (w)}(t)+μ·X _(state)(t)[X_(state) ^(H)(t)X _(state)(t)+δ(t)·I _(P×P)]⁻¹ ·{right arrow over(e)}*(t);

where {right arrow over (e)}(t)

[e(t), e(t−1), . . . , e(t−P+1]^(T) is a P-dimensional error vector,δ(t) is a regularization factor, {right arrow over (y)}(t)

[y(t), y(t−1), . . . , y(t−P+1)]^(T) is a P-dimensional vector of asignal received by the microphone; X_(state)(t)

[{right arrow over (x)}(t), {right arrow over (x)}(t−1), . . . , {rightarrow over (x)}(t−P+1)] is an L×P-dimension state matrix; I_(P×P) isP×P-dimension unit matrix, P is the order quantity of the AP algorithm.

Then, according to a system adaptation vector defined by the Equation11, there is an Equation 40, i.e.:{right arrow over (e)}(t)={right arrow over (y)}(t)−X _(state)^(H)(t){right arrow over (w)}(t)={right arrow over (∈)}(t)+{right arrowover (s)}(t)

where an Equation 41 is used to define {right arrow over (∈)}(t), thatis, {right arrow over (∈)}(t)

X_(state) ^(H)(t)·{right arrow over (Δ)}(t);

where {right arrow over (∈)}(t) is a P-dimensional systemdistortion-free error vector.

Defining {right arrow over (s)}(t) by using an Equation 42, i.e., {rightarrow over (s)}(t)

[s(t), s(t−1), . . . , s(t−P+1)]^(T);

where {right arrow over (s)}(t) is a P-dimensional near-end speechsignal vector.

An Equation 43 is easily obtained from the Equation 39, i.e.:{right arrow over (Δ)}(t+1)={right arrow over (Δ)}(t)−μ·X _(state)(t)[X_(state) ^(H)(t)X _(state)(t)+δ(t)·I _(P×P)]⁻¹ ·{right arrow over(e)}*(t)

Assuming that the reference signal x(t) is a whitened excitation sourceand the number L of the coefficients of the FIR filter of the LAEC islarge, i.e. L>>1, then the inverse matrix in the Equation 43 may beapproximately represented by the Equation 44 as follows:

$\left\lbrack {{{X_{state}^{H}(t)}{X_{state}(t)}} + {{\delta(t)} \cdot I_{P \times P}}} \right\rbrack^{- 1} \approx {\frac{1}{{L \cdot {\sigma_{x}^{2}(t)}} + {\delta(t)}}I_{P \times P}}$

where σ_(x) ²(t) is defined by the Equation 16. The Equation 44 is addedto the Equation 43 to obtain an Equation 45:{right arrow over (Δ)}(t+1)≈{right arrow over (Δ)}(t)−μ·X_(state)(t)·{right arrow over (e)}*(t)/[L·σ _(x) ²(t)+δ(t)]

Hence there is an Equation 46:

${E\left\{ {{\overset{\rightarrow}{\Delta}\left( {t + 1} \right)}}_{2}^{2} \right\}} \approx {{E\left\{ {{\overset{\rightarrow}{\Delta}(t)}}_{2}^{2} \right\}} - \frac{2\;{\mu\; \cdot P \cdot {\sigma_{\epsilon}^{2}(t)}}}{{L \cdot {\sigma_{x}^{2}(t)}} + {\delta(t)}} + \frac{\mu^{2} \cdot L \cdot P \cdot {\sigma_{x}^{2}(t)} \cdot {\sigma_{e}^{2}(t)}}{\left\lbrack {{L \cdot {\sigma_{x}^{2}(t)}} + {\delta(t)}} \right\rbrack^{2}}}$

where σ_(∈) ²(t) and σ_(e) ²(t) are defined by the Equation 18 and theEquation 19, respectively.

Assuming that u=1, then taking a partial derivative to δ(t) in theEquation 46, and setting the partial derivative to zero to obtain anoptimal δ(t), noting the optimal δ(t) as δ_(AP) ^(opt)(t), then δ_(AP)^(opt)(t) can be expressed by using an Equation 47 as follows:

${\Delta_{AP}^{opt}(t)} = \frac{L\left\lbrack {{\sigma_{y}^{2}(t)} - {\sigma_{d}^{2}(t)}} \right\rbrack}{E\left\{ {{\overset{\rightarrow}{\Delta}(n)}}_{2}^{2} \right\}}$

By comparing the Equation 47 with the Equation 24, it is known that theoptimal time-varying regularization factor determined by the Equation 37is also applicable to the AP algorithm. Specifically, when sustainableadaptive updating of the coefficient vector of the FIR filter isperformed using the AP algorithm, the specific implementation of thestep 22 is as follows:

according to the following Equation:{right arrow over (w)}(t+1)={right arrow over (w)}(t)+μ·X _(state)(t)[X_(state) ^(H)(t)X _(state)(t)+δ^(opt)(t)·I _(P×P)]⁻¹ ·{right arrow over(e)}*(t)

applying an affine projection (AP) algorithm to sustainably adaptivelyupdate the coefficient vector of the FIR filter;

where {right arrow over (w)}(t+1) is the coefficient vector of the FIRfilter after the coefficient vector of the FIR filter is updated; {rightarrow over (w)}(t) is the coefficient vector of the FIR filter beforethe coefficient vector of the FIR filter is updated; μ is apredetermined coefficient updating step-size parameter, and 0<μ<2;δ^(opt)(t) is a time-varying regularization factor; X_(state)(t) isL×P-dimension state matrix, and X_(state)(t)=[{right arrow over (x)}(t),{right arrow over (x)}(t−1), . . . , {right arrow over (x)}(t−P+1)];{right arrow over (x)}(t−t3) is the far-end reference signal vector atthe signal sample time (t−t3), and t3=0, 1, . . . , P−1, P is the orderquantity of the AP algorithm; X_(state) ^(H)(t) is the conjugatetranspose matrix of X_(state)(t); I_(P×P) is a P×P-dimension unitmatrix; {right arrow over (e)}*(t) is a complex conjugate of {rightarrow over (e)}(t), and {right arrow over (e)}(t)={right arrow over(y)}(t)−X_(state) ^(H)(t){right arrow over (w)}(t); {right arrow over(e)}(t) is a P-dimension error vector; {right arrow over (y)}(t) is aP-dimension vector of the signal received by the microphone, and {rightarrow over (y)}(t)=[y(t), y(t−1), . . . , y(t−P+1)]^(T); y(t−t3) is thesignal received by the microphone at the signal sample time (t−t3);{right arrow over (w)}(t)=[w₀(t), w₁(t), . . . , w_(L-1)(t)]^(T),w_(t2)(t) is the (t2+1)-th coefficient of the FIR filter at the signalsample time t, t2=0, 1, 2, . . . , L−1; t is the digital-signal sampletime index number.

By applying the time-varying regularization factor δ^(opt)(t) defined bythe Equation 37 to the AP adaptive learning algorithm of the time-domainLAEC, an AP sustainable adaptive learning algorithm of the time-domainLAEC with the time-varying regularization factor can be obtained. Thealgorithm has good robustness to the “double talk” and the echo pathchange. The specific implementation flow of the algorithm is as follows:

Step 1: Initializing

1.1 Presetting parameters 0<μ<2, 0≤α_(a)<α_(d)<1, τ≥0, ρ>0, and valuesof δ_(min), L, and T_(s);

1.2 Initializing relevant variables{right arrow over (x)}(0)=0,{right arrow over (h)}(0)=0,{circumflex over(σ)}_(y) ²(0)=0,|{circumflex over (r)} _(ex)(t)|²=τ;

1.3 Setting a signal time index variable “t” to zero, that is, t=0;

Step 2: Calculating a relevant variable online

2.1 Using the Equation 26 to calculate {circumflex over (σ)}₁ ²(t);

2.2 Calculating the output e(t) of the LAEC using the Equation 7;

2.3 Calculating |{circumflex over (r)}_(ex)(t)|² using the Equation 33and the Equation 35;

2.4 Calculating {circumflex over (β)}(t)|_(Cross-correlation) using theEquation 34;

Step 3: Calculating the regularization factor δ^(opt)(t) online

3.1 Using the Equation 37 to calculate δ^(opt)(t);

Step 4: Updating the iterated coefficient vector of the FIR filter

4.1 Calculating the error vector {right arrow over (e)}(t) using theEquation 38;

4.2 Calculating the coefficient vector of the FIR filter using theEquation {right arrow over (w)}(t+1)={right arrow over(w)}(t)+μ·X_(state)(t)[X_(state)^(H)(t)X_(state)(t)+δ^(opt)(t)·I_(P×P)]⁻¹·{right arrow over (e)}*(t);

Step 5: Updating a signal frame index variable “t,” that is, t=t+1, andjumping to the Step 2.

II. Subband Domain

It is well known that an acoustic path usually changes rapidly withtime. Due to a correlation of a speech signal, an NLMS learningalgorithm of the time-domain LAEC will converge very slowly, so thealgorithm cannot track a change of an echo path in real time.Furthermore, in certain applications, such as in a handfree speakerphone, an acoustic echo path is generally long, which exacerbates theabove-mentioned drawbacks. Although a time-domain Recursive Least Square(RLS) algorithm has a fast convergence speed, it cannot be implementedon a current commercial DSP because the algorithm needs highcomputational complexity. In the time-domain Affine-Projection (AP)algorithm, a convergence speed and computational complexity of thealgorithm are compromise to some extent, but in the case where the orderP of the affine projection is large (in order to accelerate theconvergence of the AP algorithm), the computational complexity of thealgorithm also renders the algorithm to be difficult to be implementedon a current commercial DSP. This prompts people to study and explore afrequency-domain realization technology for the AEC, especially therealization technology implemented in a subband domain. In fact, in thesubband domain, certain characteristic of a speech signal and a transferfunctions of the acoustic echo path (e.g., a reduction in the number ofcoefficients of an echo path transfer function in each subband and areduction in an update rate of weights thereof, a reduction of a dynamicrange of a subband signal relative to that of a time-domain signal etc.)will benefit fast estimation and real-time tracking of the acoustic echopath transfer function by the adaptive learning algorithm. The contentdescribed below is to apply an idea of the adaptive learning algorithmof the time-domain LAEC with the time-varying regularization factordiscussed above to an adaptive learning problem of a subband-domainLAEC, an NLMS algorithm and an AP algorithm for adaptive learning of thesubband-domain LAEC with a time-varying regularization factor areproposed.

A structure of the subband-domain AEC is shown in FIG. 4, which iscomposed of an analysis filter bank (AFB), a subband-domain linear echocanceller (LAEC), a subband-domain residual echo suppressor (RES) and asynthesis filter bank (SFB), where t, n and k represent a digital signalsample time, a signal frame time index variable, and a subband indexsequence number (a subband index sequence-number variable),respectively; the AFB firstly transforms the time-domain referencesignal x(t) from a far end and the time-domain microphone signal y(t)from a near end into subband-domain signals X(k, n) and Y(k, n) beinginputted to the subband-domain LAEC for linear echo cancellation. Anoutputted signal E(k, n) of the subband-domain LAEC is processed in thesubband-domain RES and then transformed into a time-domain signal ŝ(t)by the SFB.

A structure of the LAEC in a subband k is shown in FIG. 5, where thesubband spectrum of the far-end reference signal of the subband k at thesignal frame time n is X (k, n), the subband spectrum of the signalreceived by the microphone is Y(k, n), The subband spectrum of thenear-end speech signal is S(k, n), the subband spectrum of the echosignal is D(k, n), the number of coefficients of the FIR filter isL_(s), and the outputted {circumflex over (D)}(k, n) is a linearestimation value of the echo signal D(k, n). The coefficient vector ofthe FIR filter in the k-subband at the signal frame time “n” isexpressed by anc Equation 48 as: {right arrow over (W)}_(k)(n)=[W₀(k,n), W₁(k, n), . . . , W_(L) _(s) ₋₁(k, n)]^(T), where k is a subbandindex variable, k=0, 1, 2, . . . , K−1, and k is the total number ofsubbands.

Comparing the structure of the time-domain LAEC shown in FIG. 3 with thestructure of the subband-domain LAEC shown in FIG. 5, correspondencerelationship shown in Table 1 can be obtained:

TABLE 1 Parameter correspondence relationship between time-domain LAECand subband-domain LAEC Time-domain Subband-domain Class of LAEC shownof LAEC shown signal/parameter in FIG. 3 in FIG. 5 Far-end referencex(t) X(k, n) signal A signal received by y(t) Y(k, n) the microphoneNear-end speech signal s(t) S(k, n) Echo signal d(t) D(k, n) LinearEstimation of {circumflex over (d)}(t) {circumflex over (D)}(k, n) theEcho Signal Output signal e(t) E(k, n) Residual echo ϵ(t) ε(k, n) Numberof FIR filter L L_(s) coefficients FIR filter coefficient {right arrowover (w)} (t) = {right arrow over (W)}_(k) (n) = vector [w₀ (t), w₁ (t),. . . , [W₀ (k, n), W₁ (k, n), . . . , w_(L−1) (t)]^(T) W_(L) _(s) ⁻¹(k, n)]^(T)

Table 1 shows that the subband-domain LAEC is in fact equivalent to atime-domain LAEC (along an axis of a signal frame “n”) on a fixedk-subband. Because each subband is independent of each other, then for agiven subband k, we can directly apply the adaptive algorithm abouttime-varying regularization factor discussed in the previous section tosolve the adaptive learning issue of the FIR filter in the subband k.According to the correspondence relationship of Table 1, it may beeasily known that the Equation 37 for calculating the time-varyingregularization factor in the time-domain LAEC should correspond to theEquation 49 in the subband k:

${\delta^{opt}\left( {k,n} \right)} = {\max\left\{ {\frac{{L_{s} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,n} \right)}}{{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2}}{\left. {\hat{\beta}\left( {k,n} \right)} \middle| {}_{{Cross}\text{-}{correlation}}{+ \rho} \right.},\delta_{\min}} \right\}\text{;}}$

where {circumflex over (σ)}_(Y) ²(k, n), {circumflex over (β)}(k,n)|_(Cross-correlation) and |{circumflex over (r)}_(EX)(k, n)|² are asubband power spectrum, a subband-domain coupling factor and a MagnitudeSquared Coherence (MSC) between an output signal E(k, t) of thesubband-domain LAEC and a far-end reference signal X(k, t),respectively, which are respectively derived from performing a variablesubstitution to the related Equation 26, Equation 33 to Equation 35 inthe time-domain LAEC, and are expressed by using the following Equation:

Equation 50:

${{\hat{\sigma}}_{Y}^{2}\left( {k,n} \right)} = \left\{ \begin{matrix}{{{\alpha_{a} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}} + {\left( {1 - \alpha_{a}} \right) \cdot {{Y\left( {k,n} \right)}}^{2}}},{{{if}\mspace{14mu}{{Y\left( {k,n} \right)}}^{2}} > {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}}} \\{{{\alpha_{d} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}} + {\left( {1 - \alpha_{d}} \right) \cdot {{Y\left( {k,n} \right)}}^{2}}},{otherwise}}\end{matrix} \right.$

where 0≤α_(a)<α_(d)<1 are preset recursive constant quantities.

Equation 51:

$\left. {\hat{\beta}\left( {k,n} \right)} \middle| {}_{{Cross}\text{-}{correlation}}{\approx \frac{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{E\left( {k,{n - {n\; 1}}} \right)}{X^{*}\left( {k,{n - {n\; 1}}} \right)}}}}^{2}}{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{X\left( {k,{n - {n\; 1}}} \right)}}^{2}}}^{2}}} \right.$

where N_(s) is the number of signal frames used in calculating theaverage, and N_(s)<<L_(s).

Equation 52:

${\hat{r}\left( {k,n} \right)} \approx \frac{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{E\left( {k,{n - {n\; 1}}} \right)}{X^{*}\left( {k,{n - {n\; 1}}} \right)}}}}^{2}}{\left( {\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{E\left( {k,{n - {n\; 1}}} \right)}}^{2}} \right)\left( {\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{X\left( {k,{n - {n\; 1}}} \right)}}^{2}} \right)}$

Equation 53:

${{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2} = \left\{ \begin{matrix}{{\hat{r}\left( {k,n} \right)},{{{if}\mspace{14mu}{\hat{r}\left( {k,n} \right)}} > {{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2}}} \\{{{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2},{otherwise}}\end{matrix} \right.$

Then the NLMS learning algorithm for updating FIR filter coefficientswith the time-varying regularization factor in the subband k can beexpressed by using an Equation 54 and an Equation 55 as follows:E(k,n)=Y(k,n)−{right arrow over (X)} _(k) ^(H)(n){right arrow over (W)}_(k)(n)  Equation 54:{right arrow over (W)} _(k)(n+1)={right arrow over (W)} _(k)(n)+μ·{rightarrow over (X)} _(k)(n)E*(k,n)/[{right arrow over (X)} _(k)^(H)(n){right arrow over (X)} _(k)(n)+δ^(opt))]  Equation 55:

where δ^(opt)(k, n) is a time-varying regularization factor determinedby the Equation 49; {right arrow over (X)}_(k)(n) can be defined by anEquation 56: {right arrow over (X)}_(k)(n)

[X(k, n), X(k, n−1), . . . , X(k, n−L_(s)+1)]^(T).

It is known from the above derivation that the preset signal includes asubband spectrum of the near-end speech signal received by themicrophone and inputted in the AEC and a subband spectrum of a far-endreference speech signal. When the coefficient vector of the FIR filteris a subband-domain coefficient vector of the FIR filter and thetime-varying regularization factor is a subband-domain time-varyingregularization factor, the specific implementation of the Step 21 in thepresent disclosure is as follows: obtaining a subband power spectrum ofthe signal received by the microphone and an effective estimation valueof a subband-domain coupling factor, respectively; according to thesubband power spectrum of the signal received by the microphone and theeffective estimation value of the subband-domain coupling factor,obtaining a subband-domain time-varying regularization factor used foriteratively updating the subband-domain coefficient vector of the FIRfilter when the subband-domain coefficient vector of the FIR filter isused for processing a preset signal.

Further, a method of obtaining the subband power spectrum of the signalreceived by the microphone is as follows:

According to the Equation 50:

${{\hat{\sigma}}_{Y}^{2}\left( {k,n} \right)} = \left\{ {\begin{matrix}{{{\alpha_{a} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}} + {\left( {1 - \alpha_{a}} \right) \cdot {{Y\left( {k,n} \right)}}^{2}}},{{{if}\mspace{14mu}{{Y\left( {k,n} \right)}}^{2}} > {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}}} \\{{{\alpha_{d} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}} + {\left( {1 - \alpha_{d}} \right) \cdot {{Y\left( {k,n} \right)}}^{2}}},{{{if}\mspace{14mu}{{Y\left( {k,n} \right)}}^{2}} \leq {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}}}\end{matrix},} \right.$obtaining the subband power spectrum of the signal received by themicrophone;

where {circumflex over (σ)}_(Y) ²(k, n) is the subband power spectrum ofthe signal received by the microphone; Y(k, n) is a subband spectrum ofthe signal received by the microphone; α_(a) and α_(d) are presetrecursive constant quantities, and 0·α_(a)<α_(d)<1; k is a subband indexvariable, k=0, 1, 2, . . . , K−1, and K is the total number of subbands;n is the signal frame time index variable.

Further, the method of obtaining the effective estimation value of thesubband-domain coupling factor includes: obtaining a biased estimationvalue of the subband-domain coupling factor according to across-correlation method; obtaining a correction factor used forcompensating for the biased estimation value of the subband-domaincoupling factor; obtaining an effective estimation value of thesubband-domain coupling factor according to the biased estimation valueof the subband-domain coupling factor and the correction factor.

Further, the step of obtaining the biased estimation value of thesubband-domain coupling factor according to the cross-correlation methodincludes:

according to an Equation 51:

${\left. {\hat{\beta}\left( {k,n} \right)} \right|_{{Cross}\text{-}{correlation}} = \frac{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{E\left( {k,{n - {n\; 1}}} \right)}{X^{*}\left( {k,{n - {n\; 1}}} \right)}}}}^{2}}{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{X\left( {k,{n - {n\; 1}}} \right)}}^{2}}}^{2}}},$obtaining the biased estimation value of the subband-domain couplingfactor;

where {circumflex over (β)}(k, n)|_(Cross-correlation) is a biasedestimation value of the subband-domain coupling factor; X*(k, n−n1) is acomplex conjugate of X(k, n−n1); n1=0, 1, 2, . . . , N_(s)−1, N_(s) isthe number of signal frames used for estimation of {circumflex over(β)}(k, n)|_(Cross-correlation), and N_(s)<<L_(s), L_(s) is the numberof coefficients of the FIR filter in each subband; E(k, n−n1) is asubband spectrum of an error signal outputted by the AEC at signal frametime (n−n1); E(k, n)=Y(k, n)−{right arrow over (X)}_(k) ^(H)(n){rightarrow over (W)}_(k)(n), E(k, n) is the subband spectrum of the errorsignal outputted by the AEC at signal frame time n; Y(k, n) is thesubband spectrum of the signal received by the microphone at the signalframe time n; {right arrow over (X)}_(k) ^(H)(n) is a conjugatetranspose matrix of {right arrow over (X)}_(k)(n); {right arrow over(X)}_(k)(n) is a subband-spectrum vector of the far-end referencesignal, and {right arrow over (X)}_(k)(n)=[X(k, n), X(k, n−1), . . . ,X(k, n−L_(s)+1)]^(T); X(k, n−n2) is a subband spectrum of a far-endreference signal at signal frame time (n−n2); T is a transpose operator;{right arrow over (W)}_(k)(n) is the coefficient vector of the FIRfilter in a subband k, {right arrow over (W)}_(k)(n)=[W₀(k, n), W₁(k,n), . . . , W_(L) _(s) ₋₁(k, n)]^(T); W_(n2)(k, n) is the (n2+1)-thcoefficient of the FIR filter in the subband k at the signal frame timen, n2=0, 1, 2, . . . , L_(s)−1; k is a subband index variable, k=0, 1,2, . . . , K−1, and K is the total number of subbands; n is the signalframe time index variable.

Further, the step of obtaining the correction factor used forcompensating for the biased estimation value of the subband-domaincoupling factor includes: obtaining a candidate value of a square of amagnitude of a correlation coefficient between a subband spectrum of theerror signal outputted by the AEC and a subband spectrum of a far-endreference signal; obtaining a square of an effective magnitude of acorrelation coefficient between a subband spectrum of the error signaloutputted by the AEC and a subband spectrum of the far-end referencesignal according to the candidate value of the square of the magnitudeof the correlation coefficient between the subband spectrum of the errorsignal outputted by the AEC and the subband spectrum of the far-endreference signal, and taking the square of the effective magnitude ofthe correlation coefficient between the subband spectrum of the errorsignal outputted by the AEC and the subband spectrum of the far-endreference signal as the correction factor used for compensating for thebiased estimation value of the subband-domain coupling factor.

Further, the step of obtaining the candidate value of the square of themagnitude of the correlation coefficient between the subband spectrum ofthe error signal outputted by the AEC and the subband spectrum of thefar-end reference signal includes: according to an Equation 52:

${{\hat{r}\left( {k,n} \right)} = \frac{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{E\left( {k,{n - {n\; 1}}} \right)}{X^{*}\left( {k,{n - {n\; 1}}} \right)}}}}^{2}}{\left( {\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{E\left( {k,{n - {n\; 1}}} \right)}}^{2}} \right)\left( {\sum\limits_{m = 0}^{N_{s} - 1}{{X\left( {k,{n - {n\; 1}}} \right)}}^{2}} \right)}},$obtaining the candidate value of the square of the magnitude of thecorrelation coefficient between the subband spectrum of the error signaloutputted by the AEC and the subband spectrum of the far-end referencesignal;

where {circumflex over (r)}(k, n) is the candidate value of the squareof the magnitude of the correlation coefficient between the subbandspectrum of the error signal outputted by the AEC and the subbandspectrum of the far-end reference signal; X*(k, n−n1) is a complexconjugate of X(k, n−n1); n1=0, 1, 2, . . . , N_(s)−1, N_(s) is thenumber of frames used for estimation of {circumflex over (r)}(k, n), andN_(s)<<L_(s), L_(s) is the number of coefficients of the FIR filter ineach subband; E(k, n−n1) is the subband spectrum of the error signaloutputted by the AEC at signal frame time (n−n1); E(k, n)=Y(k, n)−{rightarrow over (X)}_(k) ^(H)(n){right arrow over (W)}_(k)(n), E(k, n) is thesubband spectrum of the error signal outputted by the AEC at signalframe time n; Y(k, n) is a subband spectrum of a signal received by themicrophone; {right arrow over (X)}_(k) ^(H)(n) is a conjugate transposematrix of {right arrow over (X)}_(k)(n); {right arrow over (X)}_(k)(n)is the subband spectrum vector of the far-end reference signal, and{right arrow over (X)}_(k)(n)=[X(k, n), X(k, n−1), . . . , X(k,n−L_(s)+1)]^(T); X(k, n−n2) is the subband spectrum of the far-endreference signal at the signal frame time (n-n2); T is a transposeoperator; {right arrow over (W)}_(k)(n) is the coefficient vector of theFIR filter in a subband k; {right arrow over (W)}_(k)(n)=[W₀(k, n),W₁(k, n), . . . , W_(L) _(s) ₋₁(k, n)]^(T); W_(n2)(k, n) is a (n2+1)-thcoefficient of the FIR filter in the signal frame time n in the subbandk; n2=0, 1, 2, . . . , L_(s)−1; k is a subband index variable; k=, 1, 2,. . . , K−1, and K is the total number of subbands; n is the signalframe time index variable.

Further, the step of obtaining the square of the effective magnitude ofthe correlation coefficient between the subband spectrum of the errorsignal outputted by the AEC and the subband spectrum of the far-endreference signal according to the candidate value of the square of themagnitude of the correlation coefficient between the subband spectrum ofthe error signal outputted by the AEC and the subband spectrum of thefar-end reference signal, includes:

according to an Equation 53:

${{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2} = \left\{ {\begin{matrix}{{\hat{r}\left( {k,n} \right)},} & {{{if}\mspace{14mu}{\hat{r}\left( {k,n} \right)}} > {{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2}} \\{{{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2},} & {{{if}\mspace{14mu}{\hat{r}\left( {k,n} \right)}} \leq {{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2}}\end{matrix},} \right.$obtaining the square of the effective magnitude of the correlationcoefficient between the subband spectrum of the error signal outputtedby the AEC and the subband spectrum of the far-end reference signal;

where |{circumflex over (r)}_(EX)(k, n)|² is the square of the effectivemagnitude of the correlation coefficient between the subband spectrum ofthe error signal outputted by the AEC and the subband spectrum of thefar-end reference signal; {circumflex over (r)}(k, n) is the candidatevalue of the square of the magnitude of the correlation coefficientbetween the subband spectrum of the error signal outputted by the AECand the subband spectrum of the far-end reference signal; n is thesignal frame time index variable.

Further, the step of obtaining the effective estimation value of thesubband-domain coupling factor according to the biased estimation valueof the subband-domain coupling factor and the correction factorincludes:

based on an Equation:

${{\hat{\beta}\left( {k,n} \right)} = \frac{\left. {\hat{\beta}\left( {k,n} \right)} \right|_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2}}},$obtaining the effective estimation value of the subband-domain couplingfactor;

where {circumflex over (β)}(k, n) is the effective estimation value ofthe subband-domain coupling factor, {circumflex over (β)}(k,n)|_(Cross-correlation) is the biased estimation value of thesubband-domain coupling factor; |{circumflex over (r)}_(EX)(k, n)|² isthe square of the effective magnitude of the correlation coefficientbetween the subband spectrum of the error signal outputted by the AECand the subband spectrum of the far-end reference signal; n is thesignal frame time index variable.

Further, the step of obtaining the subband-domain time-varyingregularization factor used for iteratively updating the subband-domaincoefficient vector of the FIR filter when the subband-domain coefficientvector of the FIR filter is used for processing a preset signal,according to the subband power spectrum of the signal received by themicrophone and the effective estimation value of the subband-domaincoupling factor, includes:

according to the Equation 49:

${{\delta^{opt}\left( {k,n} \right)} = {{\max\left\{ {\frac{L_{s} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,n} \right)}}{{\overset{\hat{}}{\beta}\left( {k,n} \right)} + \rho_{0}},\delta_{\min}} \right\}} = {\max\left\{ {\frac{{L_{s} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,n} \right)}}{{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2}}{\left. {\overset{\hat{}}{\beta}\left( {k,n} \right)} \middle| {}_{{Cross}\text{-}{correlation}}{+ \rho} \right.},\delta_{\min}} \right\}}}},$obtaining a subband-domain time-varying regularization factor used foriteratively updating the subband-domain coefficient vector of the FIRfilter when the subband-domain coefficient vector of the FIR filter isused for processing a preset signal;

where δ^(opt)(k, n) is the subband-domain time-varying regularizationfactor; {circumflex over (σ)}_(Y) ²(k, n) is the subband power spectrumof the signal received by the microphone;

${\hat{\beta}\left( {k,n} \right)} = \frac{\left. {\hat{\beta}\left( {k,n} \right)} \right|_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2}}$is the effective estimation value of the subband-domain coupling factor,{circumflex over (β)}(k, n)|_(Cross-correlation) is the biasedestimation value of the subband-domain coupling factor; |{circumflexover (r)}_(EX)(k, n)|² is the square of the effective magnitude of thecorrelation coefficient between the subband spectrum of the error signaloutputted by the AEC and the subband spectrum of the far-end referencesignal; δ_(min) is the preset small real constant quantity, andδ_(min)>0; ρ₀ and ρ are preset small real constants, and ρ>0, ρ₀>0; n isthe signal frame time index variable.

Specifically, when substainably adaptively updating the coefficientvector of the FIR filter using the NLMS algorithm, the specificimplementation of the Step 22 is as follows:

according to the Equation:

{right arrow over (W)}_(k)(n+1)={right arrow over (W)}_(k)(n)+μ·{rightarrow over (X)}_(k)(n)E*(k, n)/[{right arrow over (X)}_(k) ^(H)(n){rightarrow over (X)}_(k)(n)+δ^(opt)(k, n)] sustainably adaptively updatingthe subband-domain coefficient vector of the FIR filter using aNormalized Least Mean Square (NLMS) algorithm;

where {right arrow over (W)}_(k)(n+1) is the coefficient vector of theFIR filter in the subband k after the coefficient vector of the FIRfilter is updated; {right arrow over (W)}_(k)(n) is the coefficientvector of the FIR filter in the subband k before the coefficient vectorof the FIR filter is updated; μ is a predetermined coefficient updatingstep-size parameter, and 0<μ<2; {right arrow over (X)}_(k)(n) is thesubband spectrum vector of the far-end reference signal; {right arrowover (X)}_(k)(n)=[X(k, n), X(k, n−1), . . . , X(k, n−L_(s)+1)]^(T); X(k,n−n2) is the subband spectrum of the far-end reference signal at thesignal frame time (n−n2); n2=0, 1, . . . , L_(s)−1, L_(s) is the numberof coefficients of the FIR filter in each subband, T is the transposeoperator; {right arrow over (X)}_(k) ^(H)(n) is a conjugate transposematrix of {right arrow over (X)}_(k)(n); E*(k, n) is the complexconjugate of E(k, n); E(k, n) is the subband spectrum of the errorsignal outputted by AEC at the signal frame time “n”, and E(k, n)=Y(k,n)−{right arrow over (X)}_(k) ^(H)(n){right arrow over (W)}_(k)(n); Y(k,n) is the subband spectrum of the signal received by the microphone atthe signal frame time “n”; {right arrow over (W)}_(k)(n) [W₀(k, n),W₁(k, n), . . . , W_(L) _(s) ₋₁(k, n)]^(T); W_(n2)(k, n) is the(n2+1)-th coefficient of the FIR filter in the subband k in the signalframe time “n”; δ^(opt)(k, n) is the subband-domain time-varyingregularization factor; k is a subband index variable, k=0, 1, 2, . . . ,K−1, and K is the total number of subbands; n is the signal frame timeindex variable.

By applying the time-varying regularization factor δ^(opt)(k, n) definedby the Equation 49 to the NLMS adaptive learning algorithm of thesubband-domain LAEC, an NLMS sustainable adaptive learning algorithmwith the time-varying regularization factor in the subband-domain LAECcan be obtained. The algorithm has good robustness to both the “doubletalk” and the echo path change. The specific implementation flow of thealgorithm is as follows:

Step 1: Initializing

1.1 Presetting parameters 0<μ<2, 0≤α_(a)<α_(d)<1, τ≥0, ρ>0, and valuesof δ_(min), L_(s), and T_(s);

1.2 Initializing relevant variables, and performing followinginitialization to all subbands k, k=0, . . . , K−1:{right arrow over (X)} _(k)(0)=0,{right arrow over (W)}_(k)(0)=0,{circumflex over (σ)}_(Y) ²(k,0)=0,|{circumflex over (r)}_(EX)(k,0)|²=τ;

1.3 Setting the signal frame index variable “n” to zero, that is, n=0;

Step 2: Performing following processing to all subbands k, k=0, . . . ,K−1:

2.1 Calculating relevant variables online

2.1.1 Using the Equation 50 to calculate {circumflex over (σ)}_(Y) ²(k,n);

2.1.2 Calculating an output E(k, n) of the LAEC using the Equation 54;

2.1.3 Calculating |{circumflex over (r)}_(EX)(k, n)|² using the Equation52 and the Equation 53;

2.1.4 Calculating {circumflex over (β)}(k, n)|_(Cross-correlation) usingthe Equation 51.

2.2 Calculating the regularization factor δ^(opt)(k, n) online

2.2.1 Calculating the δ^(opt)(k, n) using the Equation 49;

2.3 Iteratively updating the subband-domain coefficient vector of theFIR filter

2.3.1 Using the Equation {right arrow over (W)}_(k)(n+1)={right arrowover (W)}_(k)(n)+μ·{right arrow over (X)}_(k)(n)E*(k, n)/[{right arrowover (X)}_(k) ^(H)(n){right arrow over (X)}_(k)(n)+δ^(opt)(k, n)] tocalculate the coefficient vector of the FIR filter;

Step 3: Updating the signal frame index variable “n”, i.e., n=n+1, andjumping to the step 2.

Similarly, a time-varying regularization factor AP algorithm forupdating the coefficients of the FIR filter in the subband k can beexpressed by using the Equation 57 and the Equation 58, respectively, asfollow: {right arrow over (E)}_(k)(n)={right arrow over(Y)}_(k)(n)−X_(state) ^(H)(k, n){right arrow over (W)}_(k)(n); {rightarrow over (W)}_(k)(n+1)={right arrow over (W)}_(k)(n)+λ·X_(state)(k,n)·

[X_(state) ^(H)(k, n)X_(state)(k, n)+δ^(opt)(k, n)·I_(P×P)]⁻¹·{rightarrow over (E)}_(k)*(n),

where, {right arrow over (E)}_(k)(n)

[E(k, n), E(k, n−1), . . . , E(k, n−P+1]^(T) is a P-dimension errorvector; {right arrow over (Y)}_(k)(n)

[Y(k, n), Y(k, n−1), . . . , Y(k, n−P+1)]^(T) is a P-dimension vector ofthe signal received by the microphone, X_(state)(k, n)

[{right arrow over (X)}_(k)(n), {right arrow over (X)}_(k)(n−1), . . . ,{right arrow over (X)}_(k)(n−P+1)] is an L×P-dimension state matrix,I_(P×P) is a P×P dimension unit matrix.

Specifically, when sustainably adaptively updating the coefficientvector of the FIR filter using the AP algorithm, the specificimplementation of the Step 22 is as follows:

according to the Equation:

{right arrow over (W)}_(k)(n+1)={right arrow over(W)}_(k)(n))+μ·X_(state)(k, n)·[X_(state) ^(H)(k, n)X_(state)(k,n)+δ^(opt)(k, n)·I_(P×P)]⁻¹·{right arrow over (E)}_(k)*(n), substainablyadaptively updating the subband-domain coefficient vector of the FIRfilter using an affine projection (AP) algorithm.

where {right arrow over (W)}_(k)(n+1) is the coefficient vector of theFIR filter in the subband k after the coefficient vector of the FIRfilter is updated; {right arrow over (W)}_(k)(n) is the coefficientvector of the FIR filter in the subband k before the coefficient vectorof the FIR filter is updated; μ is a predetermined coefficient updatingstep-size parameter, and 0<μ<2; δ^(opt)(k, n) is the subband-domaintime-varying regularization factor; X_(state)(k, n) is an L×P-dimensionstate matrix in the subband k, and X_(state)(k, n)=[{right arrow over(X)}_(k)(n), {right arrow over (X)}_(k)(n−1), . . . , {right arrow over(X)}_(k)(n−P+1)] is the subband spectrum vector of the far-end referencesignal at the signal frame time (n−n3), and n3=0, 1, . . . , P−1, P isthe order quantity of the AP algorithm; X_(state) ^(H)(k, n) is theconjugate transpose matrix of X_(state)(k, n); I_(P×P) is aP×P-dimension unit matrix; {right arrow over (E)}_(k)*(n) is the complexconjugate of {right arrow over (E)}_(k)(n), and {right arrow over(E)}_(k)(n)={right arrow over (Y)}_(k)(n)−X_(state) ^(H)(k, n){rightarrow over (W)}_(k)(n); {right arrow over (E)}_(k)(n) is the subbandspectrum vector of a P-dimensional error signal; {right arrow over(Y)}_(k)(n) is the P-dimension subband spectrum vector of the signalreceived by the microphone, and {right arrow over (Y)}_(k)(n)=[Y(k, n),Y(k, n−1), . . . , Y(k, n−P+1)]^(T); Y(k, n−n3) is the signal receivedby the microphone at the signal frame time (n−n3); {right arrow over(W)}_(k)(n)=[W₀(k, n), W₁(k, n), . . . , W_(L) _(s) ₋₁(k, n)]^(T);W_(n2)(k, n) is the (n2+1)-th coefficient of the FIR filter in thesubband k at the signal frame time “n”, n2=0, 1, . . . , L_(s)−1, L_(s)is the number of coefficients of the FIR filter in each subband; k is asubband index variable, k=0, 1, 2, . . . , K−1, and K is the totalnumber of subbands; n is the signal frame time index variable.

By applying the time-varying regularization factor δ^(opt)(k, n) definedby the Equation 49 to the AP adaptive learning algorithm of thesubband-domain LAEC, an AP sustainable adaptive learning algorithm withthe time-varying regularization factor in the subband-domain LAEC can beobtained. The algorithm has good robustness to both the “double talk”and the echo path change. The specific implementation flow of thealgorithm is as follows:

Step 1: Initializing

1.1 Presetting parameters 0<μ<2, 0≤α_(a)<α_(d)<1, τ>0, ρ>0, and valuesof δ_(min), L_(s) and N_(s);

1.2 Initializing relevant variables, and performing followinginitialization to all subbands k, k=0, . . . , K−1:{right arrow over (X)} _(k)(0)=0,{right arrow over (W)}_(k)(0)=0,{circumflex over (σ)}_(Y) ²(k,0)=0,|{circumflex over (r)}_(EX)(k,0)|²=τ;

1.3 Setting the signal frame index variable “n” to zero, that is, n=0;

Step 2: Performing following processing to all subbands k, k=0, . . . ,K−1:

2.1 Calculating relevant variables online

2.1.1 Using the Equation 50 to calculate {circumflex over (σ)}_(Y) ²(k,n);

2.1.2 Calculating an output E(k, n) of the LAEC using the Equation 54;

2.1.3 Calculating |{circumflex over (r)}_(EX)(k, n)|² using the Equation52 and the Equation 53;

2.1.4 Calculating {circumflex over (β)}(k, n)|_(Cross-correlation) usingthe Equation 51.

2.2 Calculating the regularization factor δ^(opt)(k, n) online

2.2.1 Calculating the δ^(opt)(k, n) using the Equation 49;

2.3 Iteratively updating the subband-domain coefficient vector of theFIR filter

2.3.1 Using an Equation 57 to calculate an error vector {right arrowover (E)}_(k)(n)

2.3.2 Using an Equation {right arrow over (W)}_(k)(n+1)={right arrowover (W)}_(k)(n)+

μ·X_(state)(k, n)·[X_(state) ^(H)(k, n)X_(state)(k, n)+δ^(opt)(k,n)·I_(P×P)]⁻¹·{right arrow over (E)}_(k)*(n) to calculate thecoefficient vector of the FIR filter;

Step 3: Updating the signal frame index variable “n”, i.e., n=n+1, andjumping to the step 2.

It should be noted that some embodiments of the present disclosure havethe following advantages:

A. the time-varying regularization factor adaptive learning algorithmsof the time-domain LAEC and the subband-domain LAEC proposed by thepresent disclosure are insensitive to an initialization process;

B. the time-varying regularization factor adaptive learning algorithmsof the time-domain LAEC and the subband-domain LAEC proposed by thepresent disclosure have good robustness to both the “double talk” in anecho cancellation application;

C. the time-varying regularization factor adaptive learning algorithmsof the time-domain LAEC and the subband-domain LAEC proposed by thepresent disclosure have good robustness to the “echo path change” in anecho cancellation application;

D. the time-varying regularization factor adaptive learning algorithm ofthe subband-domain LAEC proposed in the present disclosure reduces thecomplexity by M²/K (where M is a decimation factor and K is the totalnumber of subbands), as compared with the relevant time-domain LAEClearning algorithm. Considering that the subband spectrum of a realsignal satisfies a conjugate symmetry property, the learning algorithmof the subband-domain LAEC only needs to run on the first (K/2+1)subbands, so an algorithm complexity of the subband-domain LAEC can befurther reduced (by about a half). This learning algorithm of thesubband-domain LAEC having a low computational complexity is easy to beimplemented on a commercial DSP chip. Furthermore, in view ofparallelism of a subband-domain processing structure, the subband-domainalgorithm proposed by the present disclosure is easier to be implementedwith an Application Specific Integrated Circuit (ASIC).

As shown in FIG. 6, some embodiments of the present disclosure alsoprovide a device of sustainably adaptively updating a coefficient vectorof an Finite Impulse Response (FIR) filter. The device includes astorage 61, a processor 62 and a computer program stored on the storage61 and executable by the processor 62. The storage 61 is connected tothe processor 62 through a bus interface 63, wherein, when the processor62 executes the computer program, the processor 62 implements followingsteps: obtaining a time-varying regularization factor used foriteratively updating a coefficient vector of a FIR filter in a case thatthe coefficient vector of the FIR filter is used for processing a presetsignal; updating the coefficient vector of the FIR filter according tothe time-varying regularization factor.

Specifically, the preset signal includes one of combined pairs offollowing: a far-end reference speech signal inputted in an acousticecho canceller (AEC) and a near-end speech signal received by amicrophone; a noise reference signal and a system input signal in anadaptive noise cancellation system; an interference reference signal anda system input signal in an adaptive interference cancellation system,and an excitation input signal and an unknown system output signal to beidentified in adaptive system identification.

Further, the preset signal includes a far-end reference speech signalinputted in an AEC and a near-end speech signal received by amicrophone; when the processor 62 executes the computer program, theprocessor 62 further implements following steps: obtaining a power of asignal received by a microphone and an effective estimation value of acoupling factor; according to the power of the signal received by themicrophone and the effective estimation value of the coupling factor,obtaining a time-varying regularization factor used for iterativelyupdating the coefficient vector of the FIR filter when the coefficientvector of the FIR filter is used for processing a preset signal.

Further, when the processor 62 executes the computer program, theprocessor 62 further implements following steps:

according to an Equation:

${{\hat{\sigma}}_{y}^{2}(t)} = \left\{ \begin{matrix}{{{\alpha_{a} \cdot {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}} + {\left( {1 - \alpha_{a}} \right) \cdot {{y(t)}}^{2}}},{{{if}\mspace{14mu}{{y(t)}}^{2}} > {{\hat{\sigma}}_{y}^{2}\ \left( {t - 1} \right)}}} \\{{{\alpha_{d} \cdot {{\hat{\sigma}}_{y}^{2}\ \left( {t - 1} \right)}} + {\left( {1 - \alpha_{d}} \right) \cdot {{y(t)}}^{2}}},{{{if}\mspace{14mu}{{y(t)}}^{2}}\  \leq {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}}}\end{matrix} \right.$

obtaining the power of the signal received by the microphone;

where {circumflex over (Υ)}_(y) ²(t) is the power of the signal receivedby the microphone; y(t) is the signal received by the microphone; α_(a)and α_(d) are preset recursive constant quantities, and 0≤α_(a)<α_(d)<1;t is a digital-signal time index number.

Further, when the processor 62 executes the computer program, theprocessor 62 further implements following steps: obtaining a biasedestimation value of the coupling factor according to a cross-correlationmethod; obtaining a correction factor used for compensating for thebiased estimation value of the coupling factor; obtaining an effectiveestimation value of the coupling factor according to the biasedestimation value of the coupling factor and the correction factor.

Further, when the processor 62 executes the computer program, theprocessor 62 further implements following steps:

according to an Equation:

${\left. {\hat{\beta}(t)} \right|_{{Cross}\text{-}{correlation}} = \frac{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{e\left( {t - {t\; 1}} \right)}{x^{*}\left( {t - {t\; 1}} \right)}}}}^{2}}{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{x\left( {t - {t\; 1}} \right)}}^{2}}}^{2}}},$obtaining the biased estimation value of the coupling factor,

where {circumflex over (β)}(t)|_(Cross-correlation) is the biasedestimation value of the coupling factor based on the cross-correlationtechnique; x*(t−t1) is a complex conjugate of x(t−t1); t1=0, 1, 2, . . ., T_(s)−1, T_(s) is the number of samples used in the estimation of the{circumflex over (β)}(t)|_(Cross-correlation), and T_(s)<<L, L is thenumber of coefficients of the FIR filter; e(t−t1) is an error signaloutputted by the AEC at the signal sample time (t−t1), e(t)=y(t)−{rightarrow over (x)}^(H)(t){right arrow over (w)}(t), e(t) is an error signaloutputted by the AEC at the signal sample time t; y(t) is a signalreceived by the microphone at the signal sample time t; {right arrowover (x)}^(H)(t) is a conjugate transpose matrix of {right arrow over(x)}(t); {right arrow over (x)}(t) is the far-end reference signalvector and {right arrow over (x)}(t)=[x(t), x(t−1), . . . ,x(t−L+1)]^(T); x(t−t2) is the far-end reference signal at the signalsample time (t−t2); T is a transpose operator; {right arrow over (w)}(t)is the coefficient vector of the FIR filter, {right arrow over(w)}(t)=[w₀(t), w₁(t), . . . , w_(L-1)(t)]^(T), w_(t2)(t) is the(t2+1)-th coefficient of the FIR filter at the signal sample time t,t2=0, 1, 2, . . . . , L−1; t is a digital-signal time index number.

Further, when the processor 62 executes the computer program, theprocessor 62 further implements following steps:

obtaining a candidate value of a square of a magnitude of a correlationcoefficient between the error signal outputted by the AEC and thefar-end reference signal; obtaining a square of an effective magnitudeof the correlation coefficient between the error signal outputted by theAEC and the far-end reference signal based on the candidate value of thesquare of the magnitude of the correlation coefficient between the errorsignal outputted by the AEC and the far-end reference signal, and takingthe square of the effective magnitude of the correlation coefficientbetween the error signal outputted by the AEC and the far-end referencesignal as the correction factor used for compensating for the biasedestimation value of the coupling factor.

Further, when the processor 62 executes the computer program, theprocessor 62 further implements following steps:

according to an Equation:

${{\hat{r}(t)} = \frac{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{e\left( {t - {t\; 1}} \right)}{x^{*}\left( {t - {t\; 1}} \right)}}}}^{2}}{\left( {\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{e\left( {t - {t\; 1}} \right)}}^{2}} \right)\left( {\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{x\left( {t - {t\; 1}} \right)}}^{2}} \right)}},$obtaining the candidate value of the square of the magnitude of thecorrelation coefficient between the error signal outputted by the AECand the far-end reference signal; where {right arrow over (r)}(t) is thecandidate value of the square of the magnitude of the correlationcoefficient between the error signal outputted by the AEC and thefar-end reference signal; x(t−t1) is the far-end reference signal at thesignal sample time (t−t1); x*(t−t1) is the complex conjugate of x(t−t1);t1=0, 1, 2, . . . , T_(s)−1, T_(s) is the number of samples used in theestimation of {right arrow over (r)}(t), and T_(s)<<L, L is the numberof coefficients of the FIR filter; e(t−t1) is the error signal outputtedby the AEC at the signal sample time (t−t1), e(t)=y(t)−{right arrow over(x)}^(H)(t){right arrow over (w)}(t), e(t) is an error signal outputtedby the AEC at the signal sample time t; y(t) is the signal received bythe microphone at the signal sample time t; {right arrow over(x)}^(H)(t) is the conjugate transpose matrix of {right arrow over(x)}(t); {right arrow over (x)}(t) is the far-end reference signalvector and {right arrow over (x)}(t)=[x(t), x(t−1), . . . ,x(t−L+1)]^(T); x(t−t2) is the far-end reference signal at the signalsample time (t−t2); T is a transpose operator; {right arrow over (w)}(t)is the coefficient vector of the FIR filter, {right arrow over(w)}(t)=[w₀(t), w₁(t), . . . , w_(L-1)(t)]^(T), w_(t2)(t) (t) is the(t2+1)-th coefficient of the FIR filter at the signal sample time t,t2=0, 1, 2, . . . , L−1; t is a digital-signal sample time index number.

Further, when the processor 62 executes the computer program, theprocessor 62 further implements following steps:

according to an Equation:

${{{\hat{r}}_{ex}(t)}}^{2} = \left\{ \begin{matrix}{{\hat{r}(t)},} & {{{if}\mspace{14mu}{\hat{r}(t)}} > {{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2}} \\{{{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2},} & {{{if}\mspace{14mu}{\hat{r}(t)}} \leq {{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2}}\end{matrix} \right.$

obtaining the square of the effective magnitude of the correlationcoefficient between the signal outputted by the AEC and the far-endreference signal;

where |{right arrow over (r)}(t)|² is the square of the effectivemagnitude of the correlation coefficient between the error signaloutputted by the AEC and the far-end reference signal; {circumflex over(r)}(t) is the candidate value of the square of the magnitude of thecorrelation coefficient between the error signal outputted by the AECand the far-end reference signal; t is a digital-signal sample timeindex number.

Further, when the processor 62 executes the computer program, theprocessor 62 further implements following steps:

obtaining the effective estimation value of the coupling factoraccording to the Equation:

${{\hat{\beta}(t)} = \frac{\left. {\hat{\beta}(t)} \right|_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{ex}(t)}}^{2}}},$

where {circumflex over (β)}(t)) is the effective estimation value of thecoupling factor; {circumflex over (β)}(t)|_(Cross-correlation) is thebiased estimation value of the coupling factor based on thecross-correlation technique; |{circumflex over (r)}_(ex)(t)|² is thesquare of the effective magnitude of the correlation coefficient betweenthe error signal outputted by the AEC and the far-end reference signal;t is a digital-signal sample time index number.

Further, when the processor 62 executes the computer program, theprocessor 62 further implements following steps:

according to an Equation:

${{\delta^{opt}(t)} = {{\max\left\{ {\frac{L \cdot {{\hat{\sigma}}_{y}^{2}(t)}}{{\overset{\hat{}}{\beta}(t)} + \rho_{0}},\delta_{\min}} \right\}} = {\max\left\{ {\frac{{L \cdot {{\hat{\sigma}}_{y}^{2}(t)}}{{{\hat{r}}_{ex}(t)}}^{2}}{\left. {\overset{\hat{}}{\beta}(t)} \middle| {}_{{Cross}\text{-}{correlation}}{+ \rho} \right.},\delta_{\min}} \right\}}}},$obtaining the time-varying regularization factor used for iterativelyupdating the coefficient vector of the FIR filter; where δ^(opt)(t) isthe time-varying regularization factor; L is the number of coefficientsof the FIR filter; {circumflex over (σ)}_(y) ²(t) is the power of thesignal received by the microphone;

${\hat{\beta}(t)} = \frac{\left. {\hat{\beta}(t)} \right|_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{ex}(t)}}^{2}}$is the effective estimation value of coupling factor; {circumflex over(β)}(t)|_(Cross-correlation) is the biased estimation value of thecoupling factor based on the cross-correlation technique; |{circumflexover (r)}_(ex)(t)|² is the square of the effective magnitude of thecorrelation coefficient between the error signal outputted by the AECand the far-end reference signal; δ_(min) is a preset small realconstant quantity, and δ_(min)>0; ρ₀ and ρ are preset small realconstants, and ρ>0, ρ₀>0, respectively; t is a digital-signal sampletime index number.

Further, when the processor 62 executes the computer program, theprocessor 62 further implements following steps:

according to an Equation: {right arrow over (w)}(t+1)={right arrow over(w)}(t)+λ·{right arrow over (x)}(t)e*(t)/[{right arrow over(x)}^(H)(t){right arrow over (x)}(t)+δ^(opt)(t)], sustainably adaptivelyupdating the coefficient vector of the FIR filter by applying aNormalized Least Mean Square (NLMS) algorithm, wherein, {right arrowover (w)}(t+1) is the coefficient vector of the FIR filter after thecoefficient vector of the FIR filter is updated; {right arrow over(w)}(t) is the coefficient vector of the FIR filter before thecoefficient vector of the FIR filter is updated; μ is a predeterminedcoefficient updating step-size parameter, and 0<μ<2; {right arrow over(x)}(t) is the far-end reference signal vector and {right arrow over(x)}(t)=[x(t), x(t−1), . . . , x(t−L+1)]^(T); x(t−t2) is the far-endreference signal at the signal sample time (t−t2); T is a transposeoperator; {right arrow over (x)}^(H)(t) is the conjugate transposematrix of {right arrow over (x)}(t); δ^(opt)(t) is the time-varyingregularization factor; e*(t) is the complex conjugate of e(t);e(t)=y(t)−{right arrow over (x)}^(H)(t){right arrow over (w)}(t), e(t)is the error signal outputted by the AEC at the signal sample time t;y(t) is the signal received by the microphone at the signal sample timet; {right arrow over (w)}(t)=[w₀(t), w₁(t), . . . , w_(L-1)(t)]^(T),w_(t2)(t) is the (t2+1)-th coefficient of the FIR filter at the signalsample time t, t2=0, 1, 2, . . . , L−1; t is the digital-signal sampletime index number.

Further, when the processor 62 executes the computer program, theprocessor 62 further implements following steps:

according to an Equation:

{right arrow over (w)}(t+1)={right arrow over(w)}(t)+λ·X_(state)(t)[X_(state)^(H)(t)X_(state)(t)+δ^(opt)(t)·I_(P×P)]⁻¹·{right arrow over (e)}*(t),applying an affine projection (AP) algorithm to sustainably adaptivelyupdate the coefficient vector of the FIR filter;

where {right arrow over (w)}(t+1) is the coefficient vector of the FIRfilter after the coefficient vector of the FIR filter is updated; {rightarrow over (w)}(t) is the coefficient vector of the FIR filter beforethe coefficient vector of the FIR filter is updated; μ is apredetermined coefficient updating step-size parameter, and 0<μ<2;δ^(opt)(t) is a time-varying regularization factor; X_(state)(t) isL×P-dimension state matrix, and X_(state)(t)=[{right arrow over (x)}(t),{right arrow over (x)}(t−1), . . . , {right arrow over (x)}(t−P+1)];{right arrow over (x)}(t−t3) is the far-end reference signal vector atthe signal sample time (t−t3), and t3=0, 1, . . . , P−1, P is the orderquantity of the AP algorithm; X_(state) ^(H)(t) is the conjugatetranspose matrix of X_(state)(t); I_(P×P) is a P×P-dimension unitmatrix; {right arrow over (e)}*(t) is a complex conjugate of {rightarrow over (e)}(t), and {right arrow over (e)}(t)={right arrow over(y)}(t)−X_(state) ^(H)(t){right arrow over (w)}(t); {right arrow over(e)}(t) is a P-dimension error vector; {right arrow over (y)}(t) is aP-dimension vector of the signal received by the microphone, and {rightarrow over (y)}(t)=[y(t), y(t−1), . . . , y(t−P+1)]^(T); y(t−t3) is thesignal received by the microphone at the signal sample time (t−t3);{right arrow over (w)}(t)=[w₀(t), w₁(t), . . . , w_(L-1)(t)]^(T),w_(t2)(t) is the (t2+1)-th coefficient of the FIR filter at the signalsample time t, t2=0, 1, 2, . . . , L−1; t is a digital-signal sampletime index number.

Specifically, the preset signal includes a subband spectrum of thenear-end speech signal received by the microphone and inputted in theAEC and a subband spectrum of a far-end reference speech signal. Thecoefficient vector of the FIR filter is a subband-domain coefficientvector of the FIR filter and the time-varying regularization factor is asubband-domain time-varying regularization factor. When the processor 62executes the computer program, the processor 62 further implementsfollowing steps: obtaining a subband power spectrum of the signalreceived by the microphone and an effective estimation value of asubband-domain coupling factor, respectively; according to the subbandpower spectrum of the signal received by the microphone and theeffective estimation value of the subband-domain coupling factor,obtaining a subband-domain time-varying regularization factor used foriteratively updating the subband-domain coefficient vector of the FIRfilter when the subband-domain coefficient vector of the FIR filter isused for processing a preset signal.

Further, when the processor 62 executes the computer program, theprocessor 62 further implements following steps:

according to an Equation:

${{\hat{\sigma}}_{Y}^{2}\left( {k,n} \right)} = \left\{ {\begin{matrix}{{{\alpha_{a} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}} + {\left( {1 - \alpha_{a}} \right) \cdot {{Y\left( {k,n} \right)}}^{2}}},{{{if}\mspace{14mu}{{Y\left( {k,n} \right)}}^{2}} > {{\hat{\sigma}}_{Y}^{2}\ \left( {k,{n - 1}} \right)}}} \\{{{\alpha_{d} \cdot {{\hat{\sigma}}_{Y}^{2}\ \left( {k,{n - 1}} \right)}} + {\left( {1 - \alpha_{d}} \right) \cdot {{Y\left( {k,n} \right)}}^{2}}},{{{if}\mspace{14mu}{{Y\left( {k,n} \right)}}^{2}}\  \leq {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}}}\end{matrix},} \right.$obtaining the subband power spectrum of the signal received by themicrophone;

where {circumflex over (σ)}_(Y) ²(k, n) is the subband power spectrum ofthe signal received by the microphone; Y(k, n) is a subband spectrum ofthe signal received by the microphone; α_(a) and α_(d) are presetrecursive constant quantities, and 0≤α_(a)<α_(d)<1; k is a subband indexvariable, k=0, 1, 2, . . . , K−1, and K is the total number of subbands;n is the signal frame time index variable.

Further, when the processor 62 executes the computer program, theprocessor 62 further implements following steps:

obtaining a biased estimation value of the subband-domain couplingfactor according to a cross-correlation method; obtaining a correctionfactor used for compensating for the biased estimation value of thesubband-domain coupling factor; obtaining an effective estimation valueof the subband-domain coupling factor according to the biased estimationvalue of the subband-domain coupling factor and the correction factor.

Further, when the processor 62 executes the computer program, theprocessor 62 further implements following steps:

according to an Equation:

${\left. {\hat{\beta}\left( {k,n} \right)} \right|_{{Cross}\text{-}{correlation}} = \frac{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{E\left( {k,{n - {n\; 1}}} \right)}{X^{*}\left( {k,{n - {n\; 1}}} \right)}}}}^{2}}{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{X\left( {k,{n - {n\; 1}}} \right)}}^{2}}}^{2}}},$obtaining the biased estimation value of the subband-domain couplingfactor;

where {circumflex over (β)}(k, n)|_(Cross-correlation) is a biasedestimation value of the subband-domain coupling factor; X*(k, n−n1) is acomplex conjugate of X(k, n−n1); n1=0, 1, 2, . . . , N_(s)−1, N_(s) isthe number of signal frames used for estimation of {circumflex over(β)}(k, n)|_(Cross-correlation), and N_(s)<<L_(s), L_(s) is the numberof coefficients of the FIR filter in each subband; E(k, n−n1) is asubband spectrum of an error signal outputted by the AEC at signal frametime (n−n1); E(k, n)=Y(k, n)−{right arrow over (X)}_(k) ^(H)(n){rightarrow over (W)}_(k)(n), E(k, n) is the subband spectrum of the errorsignal outputted by the AEC at signal frame time n; Y(k, n) is thesubband spectrum of the signal received by the microphone at the signalframe time n; {right arrow over (X)}_(k) ^(H)(n) is a conjugatetranspose matrix of {right arrow over (X)}_(k)(n); {right arrow over(X)}_(k)(n) is a subband-spectrum vector of the far-end referencesignal, and {right arrow over (X)}_(k)(n)=[X(k, n), X(k, n−1), . . . ,X(k, n−L_(s)+1)]^(T); X(k, n−n2) is a subband spectrum of a far-endreference signal at signal frame time (n−n2); T is a transpose operator;{right arrow over (W)}_(k)(n) is the coefficient vector of the FIRfilter in a subband k, {right arrow over (W)}_(k)(n)=[W₀(k, n), W₁(k,n), . . . , W_(L) _(s) ₋₁(k, n)]^(T); W_(n2)(k, n) is the (n2+1)-thcoefficient of the FIR filter in the subband k at the signal frame timen, n2=0, 1, 2, . . . , L_(s)−1; k is a subband index variable, k=0, 1,2, . . . , K−1, and K is the total number of subbands; n is the signalframe time index variable.

Further, when the processor 62 executes the computer program, theprocessor 62 further implements following steps:

obtaining a candidate value of a square of a magnitude of a correlationcoefficient between a subband spectrum of the error signal outputted bythe AEC and a subband spectrum of a far-end reference signal; obtaininga square of an effective magnitude of a correlation coefficient betweena subband spectrum of the error signal outputted by the AEC and asubband spectrum of the far-end reference signal according to thecandidate value of the square of the magnitude of the correlationcoefficient between the subband spectrum of the error signal outputtedby the AEC and the subband spectrum of the far-end reference signal, andtaking the square of the effective magnitude of the correlationcoefficient between the subband spectrum of the error signal outputtedby the AEC and the subband spectrum of the far-end reference signal asthe correction factor used for compensating for the biased estimationvalue of the subband-domain coupling factor.

Further, when the processor 62 executes the computer program, theprocessor 62 further implements following steps:

according to an Equation:

${{\hat{r}\left( {k,n} \right)} = \frac{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{E\left( {k,{n - {n\; 1}}} \right)}{X^{*}\left( {k,{n - {n\; 1}}} \right)}}}}^{2}}{\left( {\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{E\left( {k,{n - {n\; 1}}} \right)}}^{2}} \right)\left( {\sum\limits_{m = 0}^{N_{s} - 1}{{X\left( {k,{n - {n\; 1}}} \right)}}^{2}} \right)}},$obtaining the candidate value of the square of the magnitude of thecorrelation coefficient between the subband spectrum of the error signaloutputted by the AEC and the subband spectrum of the far-end referencesignal;

where {circumflex over (r)}(k, n) is the candidate value of the squareof the magnitude of the correlation coefficient between the subbandspectrum of the error signal outputted by the AEC and the subbandspectrum of the far-end reference signal; X*(k, n−n1) is a complexconjugate of X(k, n−n1); n1=0, 1, 2, . . . , N_(s)−1, N_(s) is thenumber of frames used for estimation of {circumflex over (r)}(k, n), andN_(s)<<L_(s), L_(s) is the number of coefficients of the FIR filter ineach subband; E(k, n−n1) is the subband spectrum of the error signaloutputted by the AEC at signal frame time (n−n1); E(k, n)=Y(k, n)−{rightarrow over (X)}_(k) ^(H)(n){right arrow over (W)}_(k)(n), E(k, n) is thesubband spectrum of the error signal outputted by the AEC at signalframe time n; Y(k, n) is a subband spectrum of a signal received by themicrophone; {right arrow over (X)}_(k) ^(H)(n) is a conjugate transposematrix of {right arrow over (X)}_(k)(n); {right arrow over (X)}_(k)(n)is the subband spectrum vector of the far-end reference signal, and{right arrow over (X)}_(k)(n)=[X(k, n), X(k, n−1), . . . , X(k,n−L_(s)+1)]^(T); X(k, n−n2) is the subband spectrum of the far-endreference signal at the signal frame time (n−n2); T is a transposeoperator; {right arrow over (W)}_(k)(n) is the coefficient vector of theFIR filter in a subband k; {right arrow over (W)}_(k)(n)=[W₀(k, n),W₁(k, n), . . . , W_(L) _(s) ₋₁(k, n)]^(T); W_(n2)(k, n) is a (n2+1)-thcoefficient of the FIR filter in the signal frame time n in the subbandk; n2=0, 1, 2, . . . , L_(s)−1; k is a subband index variable; k=, 1, 2,. . . , K−1, and K is the total number of subbands; n is the signalframe time index variable.

Further, when the processor 62 executes the computer program, theprocessor 62 further implements following steps:

according to an Equation:

${{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2} = \left\{ {\begin{matrix}{{\hat{r}\left( {k,n} \right)},} & {{{if}\mspace{14mu}{\hat{r}\left( {k,n} \right)}} > {{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2}} \\{{{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2},} & {{{if}\mspace{14mu}{\hat{r}\left( {k,n} \right)}} \leq {{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2}}\end{matrix},} \right.$obtaining the square of the effective magnitude of the correlationcoefficient between the subband spectrum of the error signal outputtedby the AEC and the subband spectrum of the far-end reference signal;

where |{circumflex over (r)}_(E)x(k, n)|² is the square of the effectivemagnitude of the correlation coefficient between the subband spectrum ofthe error signal outputted by the AEC and the subband spectrum of thefar-end reference signal; {right arrow over (r)}(k, n) is the candidatevalue of the square of the magnitude of the correlation coefficientbetween the subband spectrum of the error signal outputted by the AECand the subband spectrum of the far-end reference signal; n is thesignal frame time index variable.

Further, when the processor 62 executes the computer program, theprocessor 62 further implements following steps:

according to an Equation:

${{\hat{\beta}\left( {k,n} \right)} = \frac{\left. {\hat{\beta}\left( {k,n} \right)} \right|_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2}}},$obtaining the effective estimation value of the subband-domain couplingfactor;

where {circumflex over (β)}(k, n) is the effective estimation value ofthe subband-domain coupling factor, {circumflex over (β)}(k,n)|_(Cross-correlation) is the biased estimation value of thesubband-domain coupling factor; |{circumflex over (r)}_(EX)(k, n)|² isthe square of the effective magnitude of the correlation coefficientbetween the subband spectrum of the error signal outputted by the AECand the subband spectrum of the far-end reference signal; n is thesignal frame time index variable.

Further, when the processor 62 executes the computer program, theprocessor 62 further implements following steps:

according to the Equation:

${{\delta^{opt}\left( {k,n} \right)} = {{\max\left\{ {\frac{L_{s} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,n} \right)}}{{\hat{\beta}\left( {k,n} \right)} + \rho_{0}},\delta_{\min}} \right\}} = {\max\left\{ {\frac{{L_{s} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,n} \right)}}{{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2}}{\left. {\hat{\beta}\left( {k,n} \right)} \middle| {}_{{Cross}\text{-}{correlation}}{+ \rho} \right.},\delta_{\min}} \right\}}}},$obtaining a subband-domain time-varying regularization factor used foriteratively updating the subband-domain coefficient vector of the FIRfilter when the subband-domain coefficient vector of the FIR filter isused for processing a preset signal;

where δ^(opt)(k, n) is the subband-domain time-varying regularizationfactor; {circumflex over (σ)}_(Y) ²(k, n) is the subband power spectrumof the signal received by the microphone;

${\hat{\beta}\left( {k,n} \right)} = \frac{\left. {\hat{\beta}\left( {k,n} \right)} \right|_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2}}$is the effective estimation value of the subband-domain coupling factor,{circumflex over (β)}(k, n)|_(Cross-correlation) is the biasedestimation value of the subband-domain coupling factor; |{circumflexover (r)}_(EX)(k, n)|² is the square of the effective magnitude of thecorrelation coefficient between the subband spectrum of the error signaloutputted by the AEC and the subband spectrum of the far-end referencesignal; δ_(min) is the preset small real constant quantity, andδ_(min)>0; ρ₀ and ρ are preset small real constants, and ρ>0, ρ₀>0; n isthe signal frame time index variable.

Further, when the processor 62 executes the computer program, theprocessor 62 further implements following steps:

according to an Equation:

{right arrow over (W)}_(k)(n+1)={right arrow over (W)}_(k)(n)+μ·{rightarrow over (X)}_(k)(n)E*(k, n)/[{right arrow over (X)}_(k) ^(H)(n){rightarrow over (X)}_(k)(n)+δ^(opt)(k, n)], sustainably adaptively updatingthe subband-domain coefficient vector of the FIR filter using aNormalized Least Mean Square (NLMS) algorithm;

where {right arrow over (W)}_(k)(n+1) is the coefficient vector of theFIR filter in the subband k after the coefficient vector of the FIRfilter is updated; {right arrow over (W)}_(k)(n) is the coefficientvector of the FIR filter in the subband k before the coefficient vectorof the FIR filter is updated; μ is a predetermined coefficient updatingstep-size parameter, and 0<μ<2; {right arrow over (X)}_(k)(n) is thesubband spectrum vector of the far-end reference signal; {right arrowover (X)}_(k)(n)=[X(k, n), X(k, n−1), . . . , X(k, n−L_(s)+1)]^(T); X(k,n−n2) is the subband spectrum of the far-end reference signal at thesignal frame time (n−n2); n2=0, 1, . . . , L_(s)−1, L_(s) is the numberof coefficients of the FIR filter in each subband, T is the transposeoperator; {right arrow over (X)}_(k) ^(H)(n) is a conjugate transposematrix of {right arrow over (X)}_(k)(n); E*(k, n) is the complexconjugate of E(k, n); E(k, n) is the subband spectrum of the errorsignal outputted by AEC at the signal frame time “n”, and E(k, n)=Y(k,n)−{right arrow over (X)}_(k) ^(H)(n){right arrow over (W)}_(k)(n); Y(k,n) is the subband spectrum of the signal received by the microphone atthe signal frame time “n”; {right arrow over (W)}_(k)(n)=[W₀(k, n),W₁(k, n), . . . , W_(L) _(s) ₋₁(k, n)]^(T); W_(n2)(k, n) is the(n2+1)-th coefficient of the FIR filter in the subband k at the signalframe time “n”; δ^(opt)(k, n) is the subband-domain time-varyingregularization factor; k is a subband index variable, k=, 1, 2, . . . ,K−1, and K is the total number of subbands; n is the signal frame timeindex variable.

Further, when the processor 62 executes the computer program, theprocessor 62 further implements following steps:

according to an Equation:

{right arrow over (W)}_(k)(n+1)={right arrow over(W)}_(k)(n)+μ·X_(state)(k, n)·[X_(state) ^(H)(k, n)X_(state)(k,n)+δ^(opt)(k, n)·I_(P×P)]⁻¹·{right arrow over (E)}_(k)*(n), substainablyadaptively updating the subband-domain coefficient vector of the FIRfilter using an affine projection (AP) algorithm;

where {right arrow over (W)}_(k)(n+1) is the coefficient vector of theFIR filter in the subband k after the coefficient vector of the FIRfilter is updated; {right arrow over (W)}_(k)(n) is the coefficientvector of the FIR filter in the subband k before the coefficient vectorof the FIR filter is updated; μ is a predetermined coefficient updatingstep-size parameter, and 0<μ<2; δ^(opt)(k, n) is the subband-domaintime-varying regularization factor; X_(state)(k, n) is an L×P-dimensionstate matrix in the subband k, and X_(state)(k, n)=[{right arrow over(X)}_(k)(n), {right arrow over (X)}_(k)(n−1), . . . , {right arrow over(X)}_(k)(n−P+1)] is the subband spectrum vector of the far-end referencesignal at the signal frame time (n−n3), and n3=0, 1, . . . , P−1, P isthe order quantity of the AP algorithm; X_(state) ^(H)(k, n) is theconjugate transpose matrix of X_(state)(k, n); I_(P×P) is aP×P-dimension unit matrix; {right arrow over (E)}_(m)*(n) is the complexconjugate of {right arrow over (E)}_(k)(n), and {right arrow over(E)}_(k)(n)={right arrow over (Y)}_(k)(n)−X_(state) ^(H)(k, n){rightarrow over (W)}_(k)(n); {right arrow over (E)}_(k)*(n) is the subbandspectrum vector of a P-dimensional error signal; {right arrow over(Y)}_(k)(n) is the P-dimension subband spectrum vector of the signalreceived by the microphone, and {right arrow over (Y)}_(k)(n)=[Y(k, n),Y(k, n−1), . . . , Y(k, n−P+1)]^(T); Y(k, n−n3) is the signal receivedby the microphone at the signal frame time (n−n3); {right arrow over(W)}_(k)(n)=[W₀(k, n), W₁(k, n), . . . , W_(L) _(s) ₋₁(k, n)]^(T);W_(n2)(k, n) is the (n2+1)-th coefficient of the FIR filter in thesubband k at the signal frame time “n”, n2=0, 1, . . . , L_(s)−1, L_(s)is the number of coefficients of the FIR filter in each subband; k is asubband index variable, k=0, 1, 2, . . . , K−1, and K is the totalnumber of subbands; n is the signal frame time index variable.

It will be understood by those skilled in the art that all or part ofthe steps of implementing the above embodiments may be accomplished byhardware, or may be accomplished by a computer program indicatingassociated hardware; the computer program includes instructions forperforming some or all of the steps of the method described above; andthe computer program may be stored in a readable storage medium, whichmay be any form of storage medium.

As shown in FIG. 7, some embodiments of the present disclosure alsoprovide a device 70 of sustainably adaptively updating a coefficientvector of a Finite Impulse Response (FIR) filter. The device 70 includesan obtaining module, configured to obtain a time-varying regularizationfactor used for iteratively updating a coefficient vector of a FIRfilter in a case that the coefficient vector of the FIR filter is usedfor processing a preset signal; an updating module, configured to updatethe coefficient vector of the FIR filter according to the time-varyingregularization factor.

Specifically, the preset signal includes a far-end reference speechsignal inputted in an AEC and a near-end speech signal received by amicrophone; the obtaining module 71 includes: a first obtaining unit,configured to obtain a power of a signal received by a microphone and aneffective estimation value of a coupling factor; a second obtainingunit, configured to, according to the power of the signal received bythe microphone and the effective estimation value of the couplingfactor, obtain a time-varying regularization factor used for iterativelyupdating the coefficient vector of the FIR filter when the coefficientvector of the FIR filter is used for processing a preset signal.

Further, a manner of obtaining the power of the signal received by themicrophone performed by the first obtaining unit is as follows:

according to an Equation:

${{\hat{\sigma}}_{y}^{2}(t)} = \left\{ \begin{matrix}{{{\alpha_{a} \cdot {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}} + {\left( {1 - \alpha_{a}} \right) \cdot {{y(t)}}^{2}}},{{{if}\mspace{14mu}{{y(t)}}^{2}} > {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}}} \\{{{\alpha_{d} \cdot {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}} + {\left( {1 - \alpha_{d}} \right) \cdot {{y(t)}}^{2}}},{{{if}\mspace{14mu}{{y(t)}}^{2}} \leq {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}}}\end{matrix} \right.$

obtaining the power of the signal received by the microphone;

where {circumflex over (σ)}_(y) ²(t) is the power of the signal receivedby the microphone; y(t) is the signal received by the microphone; α_(a)and α_(d) are preset recursive constant quantities, and 0≤α_(a)<α_(d)<1;t is a digital-signal time index number.

Further, a manner of obtaining the effective estimation value of thecoupling factor performed by the first obtaining unit is as follows:

obtaining a biased estimation value of the coupling factor according toa cross-correlation method; obtaining a correction factor used forcompensating for the biased estimation value of the coupling factor;obtaining an effective estimation value of the coupling factor accordingto the biased estimation value of the coupling factor and the correctionfactor.

Further, a manner of obtaining the biased estimation value of thecoupling factor according to the cross-correlation method is as follows:

according to an Equation:

${{\left. {\hat{\beta}(t)} \right|_{{Cross}\text{-}{correlation}} = \frac{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{e\left( {t - {t\; 1}} \right)}{x^{*}\left( {t - {t\; 1}} \right)}}}}^{2}}{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{x\left( {t - {t\; 1}} \right)}}^{2}}}^{2}}},}\;$obtaining the biased estimation value of the coupling factor,

where {circumflex over (β)}(t)|_(Cross-correlation) is the biasedestimation value of the coupling factor based on the cross-correlationtechnique; x*(t−t1) is a complex conjugate of x(t−t1); t1=0, 1, 2, . . ., T_(s)−1, T_(s) is the number of samples used in the estimation of the{circumflex over (β)}(t)|_(Cross-correlation), and T_(s)<<L, L is thenumber of coefficients of the FIR filter; e(t−t1) is an error signaloutputted by the AEC at the signal sample time (t−t1), e(t)=y(t)−{rightarrow over (x)}^(H)(t){right arrow over (w)}(t), e(t) is an error signaloutputted by the AEC at the signal sample time t; y(t) is a signalreceived by the microphone at the signal sample time t; {right arrowover (x)}^(H)(t) is a conjugate transpose matrix of {right arrow over(x)}(t); {right arrow over (x)}(t) is the far-end reference signalvector and {right arrow over (x)}(t)=[x(t), x(t−1), . . . ,x(t−L+1)]^(T); x(t−t2) is the far-end reference signal at the signalsample time (t−t2); T is a transpose operator; {right arrow over (w)}(t)is the coefficient vector of the FIR filter, {right arrow over(w)}(t)=[w₀(t), w₁(t), . . . , w_(L-1)(t)]^(T), w_(t2)(t) is the(t2+1)-th coefficient of the FIR filter at the signal sample time t,t2=0, 1, 2, . . . . , L−1; t is a digital-signal time index number.

Further, a manner of obtaining a correction factor used for compelsatingthe biased estimation value of the coupling factor is as follows:

obtaining a candidate value of a square of a magnitude of a correlationcoefficient between the error signal outputted by the AEC and thefar-end reference signal; obtaining a square of an effective magnitudeof the correlation coefficient between the error signal outputted by theAEC and the far-end reference signal based on the candidate value of thesquare of the magnitude of the correlation coefficient between the errorsignal outputted by the AEC and the far-end reference signal, and takingthe square of the effective magnitude of the correlation coefficientbetween the error signal outputted by the AEC and the far-end referencesignal as the correction factor used for compensating for the biasedestimation value of the coupling factor.

Further, a manner of obtaining the candidate value of the square of themagnitude of the correlation coefficient between the error signaloutputted by the AEC and the far-end reference signal is as follows:

according to an Equation:

${{\hat{r}(t)} = \frac{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{e\left( {t - {t\; 1}} \right)}{x^{*}\left( {t - {t\; 1}} \right)}}}}^{2}}{\left( {\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{e\left( {t - {t\; 1}} \right)}}^{2}} \right)\left( {\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{x\left( {t - {t\; 1}} \right)}}^{2}} \right)}},$obtaining the candidate value of the square of the magnitude of thecorrelation coefficient between the error signal outputted by the AECand the far-end reference signal; where {circumflex over (r)}(t) is thecandidate value of the square of the magnitude of the correlationcoefficient between the error signal outputted by the AEC and thefar-end reference signal; x(t−t1) is the far-end reference signal at thesignal sample time (t−t1); x*(t−t1) is the complex conjugate of x(t−t1);t1=0, 1, 2, . . . , T_(s)−1, T_(s) is the number of samples used in theestimation of {circumflex over (r)}(t), and T_(s)<<L, L is the number ofcoefficients of the FIR filter; e(t−t1) is the error signal outputted bythe AEC at the signal sample time (t−t1), e(t)=y(t)−{right arrow over(x)}^(H)(t){right arrow over (w)}(t), e(t) is an error signal outputtedby the AEC at the signal sample time t; y(t) is the signal received bythe microphone at the signal sample time t; {right arrow over(x)}^(H)(t) is the conjugate transpose matrix of {right arrow over(x)}(t); {right arrow over (x)}(t) is the far-end reference signalvector and {right arrow over (x)}^(H)(t)=[x(t), x(t−1), . . . ,x(t−L+1)]^(T); x(t−t2) is the far-end reference signal at the signalsample time (t−t2); T is a transpose operator; {right arrow over (w)}(t)is the coefficient vector of the FIR filter, {right arrow over(w)}(t)=[w₀(t), w₁(t), . . . , w_(L-1)(t)]^(T), w_(t2)(t) (t) is the(t2+1)-th coefficient of the FIR filter at the signal sample time t,t2=0, 1, 2, . . . , L−1; t is a digital-signal sample time index number.

Further, a manner of obtaining the square of the effective magnitude ofthe correlation coefficient between the error signal outputted by theAEC and the far-end reference signal based on the candidate value of thesquare of the magnitude of the correlation coefficient between the errorsignal outputted by the AEC and the far-end reference signal is asfollows:

according to an Equation:

${{{\hat{r}}_{ex}(t)}}^{2} = \left\{ \begin{matrix}{{\hat{r}(t)},} & {{{if}\mspace{14mu}{\hat{r}(t)}} > {{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2}} \\{{{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2},} & {{{if}\mspace{14mu}{\hat{r}(t)}} \leq {{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2}}\end{matrix} \right.$

obtaining the square of the effective magnitude of the correlationcoefficient between the signal outputted by the AEC and the far-endreference signal;

where |{circumflex over (r)}_(ex)(t)|² is the square of the effectivemagnitude of the correlation coefficient between the error signaloutputted by the AEC and the far-end reference signal; {circumflex over(r)}(t) is the candidate value of the square of the magnitude of thecorrelation coefficient between the error signal outputted by the AECand the far-end reference signal; t is a digital-signal sample timeindex number.

Further, a manner of obtaining the effective estimation value of thecoupling factor according to the biased estimation value of the couplingfactor and the correction factor is as follows:

obtaining the effective estimation value of the coupling factoraccording to the Equation:

${{\hat{\beta}(t)} = \frac{\left. {\hat{\beta}(t)} \right|_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{ex}(t)}}^{2}}},$

where {circumflex over (β)}(t)) is the effective estimation value of thecoupling factor; {circumflex over (β)}(t)|_(Cross-correlation) is thebiased estimation value of the coupling factor based on thecross-correlation technique; |{circumflex over (r)}_(ex)(t)|² is thesquare of the effective magnitude of the correlation coefficient betweenthe error signal outputted by the AEC and the far-end reference signal;t is a digital-signal sample time index number.

Further, the second obtaining unit is configured to:

according to an Equation:

${{\delta^{opt}(t)} = {{\max\left\{ {\frac{L \cdot {{\hat{\sigma}}_{y}^{2}(t)}}{{\hat{\beta}(t)} + \rho_{0}},\delta_{\min}} \right\}} = {\max\left\{ {\frac{{L \cdot {{\hat{\sigma}}_{y}^{2}(t)}}{{{\hat{r}}_{ex}(t)}}^{2}}{\left. {\hat{\beta}(t)} \middle| {}_{{Cross}\text{-}{correlation}}{+ \rho} \right.},\delta_{\min}} \right\}}}},$obtain the time-varying regularization factor used for iterativelyupdating the coefficient vector of the FIR filter; where δ^(opt)(t) isthe time-varying regularization factor; L is the number of coefficientsof the FIR filter; {circumflex over (σ)}_(y) ²(t) is the power of thesignal received by the microphone;

${\hat{\beta}(t)} = \frac{\left. {\hat{\beta}(t)} \right|_{{Cross}\mspace{14mu}{correlation}}}{{{{\hat{r}}_{ex}(t)}}^{2}}$is the effective estimation value of coupling factor; {circumflex over(β)}(t)|_(Cross-correlation) is the biased estimation value of thecoupling factor based on the cross-correlation technique; |{circumflexover (r)}_(ex)(t)|² is the square of the effective magnitude of thecorrelation coefficient between the error signal outputted by the AECand the far-end reference signal; δ_(min) is a preset small realconstant quantity, and δ_(min)>0; ρ₀ and ρ are preset small realconstants, and ρ>0, ρ₀>0, respectively; t is a digital-signal sampletime index number.

Further, the updating module is configured to:

according to an Equation: {right arrow over (w)}(t+1)={right arrow over(w)}(t)+μ·{right arrow over (x)}(t)e*(t)/[{right arrow over(x)}^(H)(t){right arrow over (x)}(t)+δ^(opt)(t)], sustainably adaptivelyupdate the coefficient vector of the FIR filter by applying a NormalizedLeast Mean Square (NLMS) algorithm, wherein, {right arrow over (w)}(t+1)is the coefficient vector of the FIR filter after the coefficient vectorof the FIR filter is updated; {right arrow over (w)}(t) is thecoefficient vector of the FIR filter before the coefficient vector ofthe FIR filter is updated; μ is a predetermined coefficient updatingstep-size parameter, and 0<μ<2; {right arrow over (x)}(t) is the far-endreference signal vector and {right arrow over (x)}(t)=[x(t), x(t−1), . .. , x(t−L+1)]^(T); x(t−t2) is the far-end reference signal at the signalsample time (t−t2); T is a transpose operator; {right arrow over(x)}^(H)(t) is the conjugate transpose matrix of {right arrow over(x)}(t); δ^(opt)(t) is the time-varying regularization factor; e*(t) isthe complex conjugate of e(t); e(t)=y(t)−{right arrow over(x)}^(H)(t){right arrow over (w)}(t), e(t) is the error signal outputtedby the AEC at the signal sample time t; y(t) is the signal received bythe microphone at the signal sample time t; {right arrow over(w)}(t)=[w₀(t), w₁(t), . . . , w_(L-1)(t)]^(T), w_(t2)(t) is the(t2+1)-th coefficient of the FIR filter at the signal sample time t,t2=0, 1, 2, . . . , L−1; t is the digital-signal sample time indexnumber.

Further, the updating module 72 is configured to:

according to an Equation:

{right arrow over (w)}(t+1)={right arrow over(w)}(t)+μ·X_(state)(t)[X_(state)^(H)(t)X_(state)(t)+δ^(opt)(t)·I_(P×P)]⁻¹·{right arrow over (e)}*(t),apply an affine projection (AP) algorithm to sustainably adaptivelyupdate the coefficient vector of the FIR filter;

where {right arrow over (w)}(t+1) is the coefficient vector of the FIRfilter after the coefficient vector of the FIR filter is updated; {rightarrow over (w)}(t) is the coefficient vector of the FIR filter beforethe coefficient vector of the FIR filter is updated; μ is apredetermined coefficient updating step-size parameter, and 0<μ<2;δ^(opt)(t) is a time-varying regularization factor; X_(state)(t) isL×P-dimension state matrix, and X_(state)(t)=[{right arrow over (x)}(t),{right arrow over (x)}(t−1), . . . , {right arrow over (x)}(t−P+1)];{right arrow over (x)}(t−t3) is the far-end reference signal vector atthe signal sample time (t−t3), and t3=0, 1, . . . , P−1, P is the orderquantity of the AP algorithm; X_(state) ^(H)(t) is the conjugatetranspose matrix of X_(state)(t); I_(P×P) is a P×P-dimension unitmatrix; {right arrow over (e)}*(t) is a complex conjugate of {rightarrow over (e)}(t), and {right arrow over (e)}(t)={right arrow over(y)}(t)−X_(state) ^(H)(t){right arrow over (w)}(t); {right arrow over(e)}(t) is a P-dimension error vector; {right arrow over (y)}(t) is aP-dimension vector of the signal received by the microphone, and {rightarrow over (y)}(t)=[y(t), y(t−1), . . . , y(t−P+1)]^(T); y(t−t3) is thesignal received by the microphone at the signal sample time (t−t3);{right arrow over (w)}(t)=[w₀(t), w₁(t), . . . , w_(L-1)(t)]^(T),w_(t2)(t) is the (t2+1)-th coefficient of the FIR filter at the signalsample time t, t2=0, 1, 2, . . . , L−1; t is a digital-signal sampletime index number.

Specifically, the preset signal includes a subband spectrum of thenear-end speech signal received by the microphone and inputted in theAEC and a subband spectrum of a far-end reference speech signal. Thecoefficient vector of the FIR filter is a subband-domain coefficientvector of the FIR filter and the time-varying regularization factor is asubband-domain time-varying regularization factor. The obtaining module71 includes a third obtaining unit, configured to obtain a subband powerspectrum of the signal received by the microphone and an effectiveestimation value of a subband-domain coupling factor, respectively; afourth obtaining unit, configured to, according to the subband powerspectrum of the signal received by the microphone and the effectiveestimation value of the subband-domain coupling factor, obtain asubband-domain time-varying regularization factor used for iterativelyupdating the subband-domain coefficient vector of the FIR filter whenthe subband-domain coefficient vector of the FIR filter is used forprocessing a preset signal.

Further, a manner of obtaining the subband power spectrum of the signalreceived by the microphone performed by the third obtaining unit is asfollows:

according to an Equation:

${{\hat{\sigma}}_{Y}^{2}\left( {k,n} \right)} = \left\{ {\begin{matrix}{{{\alpha_{a} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}} + {\left( {1 - \alpha_{a}} \right) \cdot {{Y\left( {k,n} \right)}}^{2}}},{{{if}\mspace{14mu}{{Y\left( {k,n} \right)}}^{2}} > {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}}} \\{{{\alpha_{d} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}} + {\left( {1 - \alpha_{d}} \right) \cdot {{Y\left( {k,n} \right)}}^{2}}},{{{if}\mspace{14mu}{{Y\left( {k,n} \right)}}^{2}} \leq {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}}}\end{matrix},} \right.$obtaining the subband power spectrum of the signal received by themicrophone;

where {circumflex over (σ)}_(Y) ²(k, n) is the subband power spectrum ofthe signal received by the microphone; Y(k, n) is a subband spectrum ofthe signal received by the microphone; α_(a) and α_(d) are presetrecursive constant quantities, and 0≤α_(a)<α_(d)<1; k is a subband indexvariable, k=0, 1, 2, . . . , K−1, and K is the total number of subbands;n is the signal frame time index variable.

Further, a manner of obtaining the effective estimation value of thesubband-domain coupling factor performed by the third obtaining unit isas follows:

obtaining a biased estimation value of the subband-domain couplingfactor according to a cross-correlation method; obtaining a correctionfactor used for compensating for the biased estimation value of thesubband-domain coupling factor; obtaining an effective estimation valueof the subband-domain coupling factor according to the biased estimationvalue of the subband-domain coupling factor and the correction factor.

Further, a manner of obtaining the biased estimation value of thesubband-domain coupling factor according to the cross-correlation methodis as follows:

according to an Equation:

${\left. {\hat{\beta}\left( {k,n} \right)} \right|_{{Cross}\text{-}{correlation}} = \frac{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{E\left( {k,{n - {n\; 1}}} \right)}{X^{*}\left( {k,{n - {n\; 1}}} \right)}}}}^{2}}{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{X\left( {k,{n - {n\; 1}}} \right)}}^{2}}}^{2}}},$obtaining the biased estimation value of the subband-domain couplingfactor;

where {circumflex over (β)}(k, n)|_(Cross-correlation) is a biasedestimation value of the subband-domain coupling factor; X*(k, n−n1) is acomplex conjugate of X(k, n−n1); n1=0, 1, 2, . . . , N_(s)−1, N_(s) isthe number of signal frames used for estimation of {circumflex over(β)}(k, n)|_(Cross-correlation), and N_(s)<<L_(s), L_(s) is the numberof coefficients of the FIR filter in each subband; E(k, n−n1) is asubband spectrum of an error signal outputted by the AEC at signal frametime (n−n1); E(k, n)=Y(k, n)−{right arrow over (X)}_(k) ^(H)(n){rightarrow over (W)}_(k)(n), E(k, n) is the subband spectrum of the errorsignal outputted by the AEC at signal frame time n; Y(k, n) is thesubband spectrum of the signal received by the microphone at the signalframe time n; {right arrow over (X)}_(k) ^(H)(n) is a conjugatetranspose matrix of {right arrow over (X)}_(k)(n); {right arrow over(X)}_(k)(n) is a subband-spectrum vector of the far-end referencesignal, and {right arrow over (X)}_(k)(n)=[X(k, n), X(k, n−1), . . . ,X(k, n−L_(s)+1)]^(T); X(k, n−n2) is a subband spectrum of a far-endreference signal at signal frame time (n−n2); T is a transpose operator;{right arrow over (W)}_(k)(n) is the coefficient vector of the FIRfilter in a subband k, {right arrow over (W)}_(k)(n)=[W₀(k, n), W₁(k,n), . . . , W_(L) _(s) ₋₁(k, n)]^(T); W_(n2)(k, n) is the (n2+1)-thcoefficient of the FIR filter in the subband k at the signal frame timen, n2=0, 1, 2, . . . , L_(s)−1; k is a subband index variable, k=0, 1,2, . . . , K−1, and K is the total number of subbands; n is the signalframe time index variable.

Further, a manner of obtaining the correction factor used forcompensating for the biased estimation value of the subband-domaincoupling factor is as follows:

obtaining a candidate value of a square of a magnitude of a correlationcoefficient between a subband spectrum of the error signal outputted bythe AEC and a subband spectrum of a far-end reference signal; obtaininga square of an effective magnitude of a correlation coefficient betweena subband spectrum of the error signal outputted by the AEC and asubband spectrum of the far-end reference signal according to thecandidate value of the square of the magnitude of the correlationcoefficient between the subband spectrum of the error signal outputtedby the AEC and the subband spectrum of the far-end reference signal, andtaking the square of the effective magnitude of the correlationcoefficient between the subband spectrum of the error signal outputtedby the AEC and the subband spectrum of the far-end reference signal asthe correction factor used for compensating for the biased estimationvalue of the subband-domain coupling factor.

Further, a manner of obtaining the candidate value of the square of themagnitude of the correlation coefficient between the subband spectrum ofthe error signal outputted by the AEC and the subband spectrum of thefar-end reference signal is as follows:

according to an Equation:

${{\hat{r}\left( {k,n} \right)} = \frac{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{E\left( {k,{n - {n\; 1}}} \right)}{X^{*}\left( {k,{n - {n\; 1}}} \right)}}}}^{2}}{\left( {\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{E\left( {k,{n - {n\; 1}}} \right)}}^{2}} \right)\left( {\sum\limits_{m = 0}^{N_{s} - 1}{{X\left( {k,{n - {n\; 1}}} \right)}}^{2}} \right)}},$obtaining the candidate value of the square of the magnitude of thecorrelation coefficient between the subband spectrum of the error signaloutputted by the AEC and the subband spectrum of the far-end referencesignal;

where {right arrow over (r)}(k, n) is the candidate value of the squareof the magnitude of the correlation coefficient between the subbandspectrum of the error signal outputted by the AEC and the subbandspectrum of the far-end reference signal; X*(k, n−n1) is a complexconjugate of X(k, n−n1); n1=0, 1, 2, . . . , N_(s)−1, N_(s) is thenumber of frames used for estimation of {right arrow over (r)}(k, n),and N_(s)<<L_(s), L_(s) is the number of coefficients of the FIR filterin each subband; E(k, n−n1) is the subband spectrum of the error signaloutputted by the AEC at signal frame time (n−n1); E(k, n)=Y(k, n)−{rightarrow over (X)}_(k) ^(H)(n){right arrow over (W)}_(k)(n), E(k, n) is thesubband spectrum of the error signal outputted by the AEC at signalframe time n; Y(k, n) is a subband spectrum of a signal received by themicrophone; {right arrow over (X)}_(k) ^(H)(n) is a conjugate transposematrix of {right arrow over (X)}_(k)(n); {right arrow over (X)}_(k)(n)is the subband spectrum vector of the far-end reference signal, and{right arrow over (X)}_(k)(n)=[X(k, n), X(k, n−1), . . . , X(k,n−L_(s)+1)]^(T); X(k, n−n2) is the subband spectrum of the far-endreference signal at the signal frame time (n−n2); T is a transposeoperator; {right arrow over (W)}_(k)(n) is the coefficient vector of theFIR filter in a subband k; {right arrow over (W)}_(k)(n)=[W₀(k, n),W₁(k, n), . . . , W_(L) _(s) ₋₁(k, n)]^(T); W_(n2)(k, n) is a (n2+1)-thcoefficient of the FIR filter in the signal frame time n in the subbandk; n2=0, 1, 2, . . . , L_(s)−1; k is a subband index variable; k=, 1, 2,. . . , K−1, and K is the total number of subbands; n is the signalframe time index variable.

Further, a manner of obtaining the square of the effective magnitude ofthe correlation coefficient between the subband spectrum of the errorsignal outputted by the AEC and the subband spectrum of the far-endreference signal according to the candidate value of the square of themagnitude of the correlation coefficient between the subband spectrum ofthe error signal outputted by the AEC and the subband spectrum of thefar-end reference signal is as follows:

according to an Equation:

${{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2} = \left\{ {\begin{matrix}{{\hat{r}\left( {k,n} \right)},} & {{{if}\mspace{14mu}{\hat{r}\left( {k,n} \right)}} > {{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2}} \\{{{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2},} & {{{if}\mspace{14mu}{\hat{r}\left( {k,n} \right)}} \leq {{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2}}\end{matrix},} \right.$obtaining the square of the effective magnitude of the correlationcoefficient between the subband spectrum of the error signal outputtedby the AEC and the subband spectrum of the far-end reference signal;

where |{circumflex over (r)}_(EX)(k, n)|² is the square of the effectivemagnitude of the correlation coefficient between the subband spectrum ofthe error signal outputted by the AEC and the subband spectrum of thefar-end reference signal; {circumflex over (r)}(k, n) is the candidatevalue of the square of the magnitude of the correlation coefficientbetween the subband spectrum of the error signal outputted by the AECand the subband spectrum of the far-end reference signal; n is thesignal frame time index variable.

Further, a manner of obtaining the effective estimation value of thesubband-domain coupling factor according to the biased estimation valueof the subband-domain coupling factor and the correction factor is asfollows:

according to an Equation:

${{\hat{\beta}\left( {k,n} \right)} = \frac{\left. {\hat{\beta}\left( {k,n} \right)} \right|_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2}}},$obtaining the effective estimation value of the subband-domain couplingfactor;

where {circumflex over (β)}(k, n) is the effective estimation value ofthe subband-domain coupling factor, {circumflex over (β)}(k,n)|_(Cross-correlation) is the biased estimation value of thesubband-domain coupling factor; |{circumflex over (r)}_(EX)(k, n)|² isthe square of the effective magnitude of the correlation coefficientbetween the subband spectrum of the error signal outputted by the AECand the subband spectrum of the far-end reference signal; n is thesignal frame time index variable.

Further, the fourth obtaining unit is configured to:

according to the Equation:

${{\delta^{opt}\left( {k,n} \right)} = {{\max\left\{ {\frac{L_{s} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,n} \right)}}{{\overset{\hat{}}{\beta}\left( {k,n} \right)} + \rho_{0}},\delta_{\min}} \right\}} = {\max\left\{ {\frac{{L_{s} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,n} \right)}}{{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2}}{\left. {\overset{\hat{}}{\beta}\left( {k,n} \right)} \middle| {}_{{Cross}\text{-}{correlation}}{+ \rho} \right.},\delta_{\min}} \right\}}}},$obtain a subband-domain time-varying regularization factor used foriteratively updating the subband-domain coefficient vector of the FIRfilter when the subband-domain coefficient vector of the FIR filter isused for processing a preset signal;

where δ^(opt)(k, n) is the subband-domain time-varying regularizationfactor; {circumflex over (σ)}_(Y) ²(k, n) is the subband power spectrumof the signal received by the microphone;

${\hat{\beta}\left( {k,n} \right)} = \frac{\left. {\hat{\beta}\left( {k,n} \right)} \right|_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2}}$is the effective estimation value of the subband-domain coupling factor,{circumflex over (β)}(k, n)|_(Cross-correlation) is the biasedestimation value of the subband-domain coupling factor; |{circumflexover (r)}_(EX)(k, n)|² is the square of the effective magnitude of thecorrelation coefficient between the subband spectrum of the error signaloutputted by the AEC and the subband spectrum of the far-end referencesignal; δ_(min) is the preset small real constant quantity, andδ_(min)>0; ρ₀ and ρ are preset small real constants, and ρ>0, ρ₀>0; n isthe signal frame time index variable.

Further, the updating module 72 is configured to:

according to an Equation:

{right arrow over (W)}_(k)(n+1)={right arrow over (W)}_(k)(n)+μ·{rightarrow over (X)}_(k)(n)E*(k, n)/[{right arrow over (X)}_(k) ^(H)(n){rightarrow over (X)}_(k)(n)+δ^(opt)(k, n)] sustainably adaptively update thesubband-domain coefficient vector of the FIR filter using a NormalizedLeast Mean Square (NLMS) algorithm;

where {right arrow over (W)}_(k)(n+1) is the coefficient vector of theFIR filter in the subband k after the coefficient vector of the FIRfilter is updated; {right arrow over (W)}_(k)(n) is the coefficientvector of the FIR filter in the subband k before the coefficient vectorof the FIR filter is updated; μ is a predetermined coefficient updatingstep-size parameter, and 0<μ<2; {right arrow over (X)}_(k)(n) is thesubband spectrum vector of the far-end reference signal; {right arrowover (X)}_(k)(n)=[X(k, n), X(k, n−1), . . . , X(k, n−L_(s)+1)]^(T); X(k,n−n2) is the subband spectrum of the far-end reference signal at thesignal frame time (n−n2); n2=0, 1, . . . , L_(s)−1, L_(s) is the numberof coefficients of the FIR filter in each subband, T is the transposeoperator; {right arrow over (X)}_(k) ^(H)(n) is a conjugate transposematrix of {right arrow over (X)}_(k)(n); E*(k, n) is the complexconjugate of E(k, n); E(k, n) is the subband spectrum of the errorsignal outputted by AEC at the signal frame time “n”, and E(k, n)=Y(k,n)−{right arrow over (X)}_(k)(n){right arrow over (W)}_(k)(n); Y(k, n)is the subband spectrum of the signal received by the microphone at thesignal frame time “n”; {right arrow over (W)}_(k)(n)=[W₀(k, n), W₁(k,n), . . . , W_(L) _(s) ₋₁(k, n)]^(T); W_(n2)(k, n) is the (n2+1)-thcoefficient of the FIR filter in the subband k at the signal frame time“n”; δ^(opt)(k, n) is the subband-domain time-varying regularizationfactor; k is a subband index variable, k=0, 1, 2, . . . , K−1, and K isthe total number of subbands; n is the signal frame time index variable.

Further, the updating module 72 is configured to:

according to an Equation:

{right arrow over (W)}_(k)(n+1)={right arrow over(W)}_(k)(n)+μ·X_(state)(k, n)·[X_(state) ^(H)(k, n)X_(state)(k,n)+δ^(opt)(k, n)·I_(P×P)]⁻¹·{right arrow over (E)}_(k)*(n), substainablyadaptively update the subband-domain coefficient vector of the FIRfilter using an affine projection (AP) algorithm;

where {right arrow over (W)}_(k)(n+1) is the coefficient vector of theFIR filter in the subband k after the coefficient vector of the FIRfilter is updated; {right arrow over (W)}_(k)(n) is the coefficientvector of the FIR filter in the subband k before the coefficient vectorof the FIR filter is updated; μ is a predetermined coefficient updatingstep-size parameter, and 0<μ<2; δ^(opt)(k, n) is the subband-domaintime-varying regularization factor; X_(state)(k, n) is an L×P-dimensionstate matrix in the subband k, and X_(state)(k, n)=[{right arrow over(X)}_(k)(n), {right arrow over (X)}_(k)(n−1), . . . , {right arrow over(X)}_(k)(n−P+1)] is the subband spectrum vector of the far-end referencesignal at the signal frame time (n−n3), and n3=0, 1, . . . , P−1, P isthe order quantity of the AP algorithm; X_(state) ^(H)(k, n) is theconjugate transpose matrix of X_(state)(k, n); I_(P×P) is aP×P-dimension unit matrix; {right arrow over (E)}_(k)*(n) is the complexconjugate of {right arrow over (E)}_(k)(n), and {right arrow over(E)}_(k)(n)={right arrow over (Y)}_(k)(n)−X_(state) ^(H)(k, n){rightarrow over (W)}_(k)(k); {right arrow over (E)}_(k)(n) is the subbandspectrum vector of a P-dimensional error signal; {right arrow over(Y)}_(k)(n) is the P-dimension subband spectrum vector of the signalreceived by the microphone, and {right arrow over (Y)}_(k)(n)=[Y(k, n),Y(k, n−1), . . . , Y(k, n−P+1)]^(T); Y(k, n−n3) is the signal receivedby the microphone at the signal frame time (n−n3); {right arrow over(W)}_(k)(n)=[W₀(k, n), W₁(k, n), . . . , W_(L) _(s) ₋₁(k, n)]^(T);W_(n2)(k, n) is the (n2+1)-th coefficient of the FIR filter in thesubband k at the signal frame time “n”, n2=0, 1, . . . , L_(s)−1, L_(s)is the number of coefficients of the FIR filter in each subband; k is asubband index variable, k=0, 1, 2, . . . , K−1, and K is the totalnumber of subbands; n is the signal frame time index variable.

It should be noted that this device embodiment describes a devicecorresponding to the above method embodiment in a one-to-one manner, andall implementations in the above method embodiment are applicable tothis device embodiment, and the same technical effect can be achieved.

Some embodiments of the present disclosure also provide a computerreadable storage medium. The computer readable storage medium includes acomputer program stored thereon. When a processor executes the computerprogram, the processor implements following steps: obtaining atime-varying regularization factor used for iteratively updating acoefficient vector of a FIR filter in a case that the coefficient vectorof the FIR filter is used for processing a preset signal; updating thecoefficient vector of the FIR filter according to the time-varyingregularization factor.

Specifically, the preset signal includes one of combined pairs offollowing: a far-end reference speech signal inputted in an acousticecho canceller (AEC) and a near-end speech signal received by amicrophone; a noise reference signal and a system input signal in anadaptive noise cancellation system; an interference reference signal anda system input signal in an adaptive interference cancellation system,and an excitation input signal and an unknown system output signal to beidentified in adaptive system identification.

Further, the preset signal includes a far-end reference speech signalinputted in an AEC and a near-end speech signal received by amicrophone; when the processor executes the computer program, theprocessor further implements following steps: obtaining a power of asignal received by a microphone and an effective estimation value of acoupling factor; according to the power of the signal received by themicrophone and the effective estimation value of the coupling factor,obtaining a time-varying regularization factor used for iterativelyupdating the coefficient vector of the FIR filter when the coefficientvector of the FIR filter is used for processing a preset signal.

Further, when the processor executes the computer program, the processorfurther implements following steps:

according to an Equation:

${{\hat{\sigma}}_{y}^{2}(t)} = \left\{ \begin{matrix}{{{\alpha_{a} \cdot {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}} + {\left( {1 - \alpha_{a}} \right) \cdot {{y(t)}}^{2}}},{{{if}\mspace{14mu}{{y(t)}}^{2}} > {{\hat{\sigma}}_{y}^{2}\ \left( {t - 1} \right)}}} \\{{{\alpha_{d} \cdot {{\hat{\sigma}}_{y}^{2}\ \left( {t - 1} \right)}} + {\left( {1 - \alpha_{d}} \right) \cdot {{y(t)}}^{2}}},{{{if}\mspace{14mu}{{y(t)}}^{2}}\  \leq {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}}}\end{matrix} \right.$

obtaining the power of the signal received by the microphone;

where σ_(y) ²(t) is the power of the signal received by the microphone;y(t) is the signal received by the microphone; α_(a) and α_(d) arepreset recursive constant quantities, and 0≤α_(a)<α_(d)<1; t is adigital-signal time index number.

Further, when the processor executes the computer program, the processorfurther implements following steps: obtaining a biased estimation valueof the coupling factor according to a cross-correlation method;obtaining a correction factor used for compensating for the biasedestimation value of the coupling factor; obtaining an effectiveestimation value of the coupling factor according to the biasedestimation value of the coupling factor and the correction factor.

Further, when the processor executes the computer program, the processorfurther implements following steps:

according to an Equation:

${\left. {\hat{\beta}(t)} \right|_{{Cross}\text{-}{correlation}} = \frac{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{e\left( {t - {t\; 1}} \right)}{x^{*}\left( {t - {t\; 1}} \right)}}}}^{2}}{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{x\left( {t - {t\; 1}} \right)}}^{2}}}^{2}}},$obtaining the biased estimation value of the coupling factor,

where {circumflex over (β)}(t)|_(Cross-correlation) is the biasedestimation value of the coupling factor based on the cross-correlationtechnique; x*(t−t1) is a complex conjugate of x(t−t1); t1=0, 1, 2, . . ., T_(s)−1, T_(s) is the number of samples used in the estimation of the{circumflex over (β)}(t)|_(Cross-correlation), and T_(s)<<L, L is thenumber of coefficients of the FIR filter; e(t−t1) is an error signaloutputted by the AEC at the signal sample time (t−t1), e(t)=y(t)−{rightarrow over (x)}^(H)(t){right arrow over (w)}(t), e(t) is an error signaloutputted by the AEC at the signal sample time t; y(t) is a signalreceived by the microphone at the signal sample time t; {right arrowover (x)}^(H)(t) is a conjugate transpose matrix of {right arrow over(x)}(t); {right arrow over (x)}(t) is the far-end reference signalvector and {right arrow over (x)}(t)=[x(t), x(t−1), . . . ,x(t−L+1)]^(T); x(t−t2) is the far-end reference signal at the signalsample time (t−t2); T is a transpose operator; {right arrow over (w)}(t)is the coefficient vector of the FIR filter, {right arrow over(w)}(t)=[w₀(t), w₁(t), . . . , w_(L-1)(t)]^(T), w_(t2)(t) is the(t2+1)-th coefficient of the FIR filter at the signal sample time t,t2=0, 1, 2, . . . . , L−1; t is a digital-signal time index number.

Further, when the processor executes the computer program, the processorfurther implements following steps:

obtaining a candidate value of a square of a magnitude of a correlationcoefficient between the error signal outputted by the AEC and thefar-end reference signal; obtaining a square of an effective magnitudeof the correlation coefficient between the error signal outputted by theAEC and the far-end reference signal based on the candidate value of thesquare of the magnitude of the correlation coefficient between the errorsignal outputted by the AEC and the far-end reference signal, and takingthe square of the effective magnitude of the correlation coefficientbetween the error signal outputted by the AEC and the far-end referencesignal as the correction factor used for compensating for the biasedestimation value of the coupling factor.

Further, when the processor executes the computer program, the processorfurther implements following steps:

according to an Equation:

${{\hat{r}(t)} = \frac{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{e\left( {t - {t\; 1}} \right)}{x^{*}\left( {t - {t\; 1}} \right)}}}}^{2}}{\left( {\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{e\left( {t - {t\; 1}} \right)}}^{2}} \right)\left( {\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{x\left( {t - {t\; 1}} \right)}}^{2}} \right)}},$obtaining the candidate value of the square of the magnitude of thecorrelation coefficient between the error signal outputted by the AECand the far-end reference signal; where {right arrow over (r)}(t) is thecandidate value of the square of the magnitude of the correlationcoefficient between the error signal outputted by the AEC and thefar-end reference signal; x(t−t1) is the far-end reference signal at thesignal sample time (t−t1); x*(t−t1) is the complex conjugate of x(t−t1);t1=0, 1, 2, . . . , T_(s)−1, T_(s) is the number of samples used in theestimation of {circumflex over (r)}(t), and T_(s)<<L, L is the number ofcoefficients of the FIR filter; e(t−t1) is the error signal outputted bythe AEC at the signal sample time (t−t1), e(t)=y(t)−{right arrow over(x)}^(H)(t){right arrow over (w)}(t), e(t) is an error signal outputtedby the AEC at the signal sample time t; y(t) is the signal received bythe microphone at the signal sample time t; {right arrow over(x)}^(H)(t) is the conjugate transpose matrix of {right arrow over(x)}(t); {right arrow over (x)}(t) is the far-end reference signalvector and {right arrow over (x)}(t)=[x(t), x(t−1), . . . ,x(t−L+1)]^(T); x(t−t2) is the far-end reference signal at the signalsample time (t−t2); T is a transpose operator; {right arrow over (w)}(t)is the coefficient vector of the FIR filter, {right arrow over(w)}(t)=[w₀(t), w₁(t), . . . , w_(L-1)(t)]^(T), w_(t2)(t) (t) is the(t2+1)-th coefficient of the FIR filter at the signal sample time t,t2=0, 1, 2, . . . , L−1; t is a digital-signal sample time index number.

Further, when the processor executes the computer program, the processorfurther implements following steps:

according to an Equation:

${{{\hat{r}}_{ex}(t)}}^{2} = \left\{ \begin{matrix}{{\hat{r}(t)},} & {{{if}\mspace{14mu}{\hat{r}(t)}} > {{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2}} \\{{{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2},} & {{{if}\mspace{14mu}{\hat{r}(t)}} \leq {{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2}}\end{matrix} \right.$

obtaining the square of the effective magnitude of the correlationcoefficient between the signal outputted by the AEC and the far-endreference signal;

where |{circumflex over (r)}_(ex)(t)|² is the square of the effectivemagnitude of the correlation coefficient between the error signaloutputted by the AEC and the far-end reference signal; {circumflex over(r)}(t) is the candidate value of the square of the magnitude of thecorrelation coefficient between the error signal outputted by the AECand the far-end reference signal; t is a digital-signal sample timeindex number.

Further, when the processor executes the computer program, the processorfurther implements following steps:

obtaining the effective estimation value of the coupling factoraccording to the Equation:

${{\hat{\beta}(t)} = \frac{\left. {\hat{\beta}(t)} \right|_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{ex}(t)}}^{2}}},$

where {circumflex over (β)}(t)) is the effective estimation value of thecoupling factor; {circumflex over (β)}(t)|_(Cross-correlation) is thebiased estimation value of the coupling factor based on thecross-correlation technique; |{circumflex over (r)}_(ex)(t)|² is thesquare of the effective magnitude of the correlation coefficient betweenthe error signal outputted by the AEC and the far-end reference signal;t is a digital-signal sample time index number.

Further, when the processor executes the computer program, the processorfurther implements following steps:

according to an Equation:

${{\delta^{opt}(t)} = {{\max\left\{ {\frac{L \cdot {{\hat{\sigma}}_{y}^{2}(t)}}{{\overset{\hat{}}{\beta}(t)} + \rho_{0}},\delta_{\min}} \right\}} = {\max\left\{ {\frac{{L \cdot {{\hat{\sigma}}_{y}^{2}(t)}}{{{\hat{r}}_{ex}(t)}}^{2}}{\left. {\overset{\hat{}}{\beta}(t)} \middle| {}_{{Cross}\text{-}{correlation}}{+ \rho} \right.},\delta_{\min}} \right\}}}},$obtaining the time-varying regularization factor used for iterativelyupdating the coefficient vector of the FIR filter; where δ^(opt)(t) isthe time-varying regularization factor; L is the number of coefficientsof the FIR filter; {circumflex over (σ)}_(y) ²(t) is the power of thesignal received by the microphone;

${\hat{\beta}(t)} = \frac{\left. {\hat{\beta}(t)} \right|_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{ex}(t)}}^{2}}$is the effective estimation value of coupling factor; {circumflex over(β)}(t)|_(Cross-correlation) is the biased estimation value of thecoupling factor based on the cross-correlation technique; |{circumflexover (r)}_(ex)(t)|² is the square of the effective magnitude of thecorrelation coefficient between the error signal outputted by the AECand the far-end reference signal; δ_(min) is a preset small realconstant quantity, and δ_(min)>0; ρ₀ and ρ are preset small realconstants, and ρ>0, ρ₀>0, respectively; t is a digital-signal sampletime index number.

Further, when the processor executes the computer program, the processorfurther implements following steps:

according to an Equation: {right arrow over (w)}(t+1)={right arrow over(w)}(t)+μ·{right arrow over (x)}(t)e*(t)/[{right arrow over(x)}^(H)(t){right arrow over (x)}(t)+δ^(opt)(t)], sustainably adaptivelyupdating the coefficient vector of the FIR filter by applying aNormalized Least Mean Square (NLMS) algorithm, wherein, {right arrowover (w)}(t+1) is the coefficient vector of the FIR filter after thecoefficient vector of the FIR filter is updated; {right arrow over(w)}(t) is the coefficient vector of the FIR filter before thecoefficient vector of the FIR filter is updated; μ is a predeterminedcoefficient updating step-size parameter, and 0<μ<2; {right arrow over(x)}(t) is the far-end reference signal vector and {right arrow over(x)}(t)=[x(t), x(t−1), . . . , x(t−L+1)]^(T); x(t−t2) is the far-endreference signal at the signal sample time (t−t2); T is a transposeoperator; {right arrow over (x)}^(H)(t) is the conjugate transposematrix of {right arrow over (x)}(t); δ^(opt)(t) is the time-varyingregularization factor; e*(t) is the complex conjugate of e(t);e(t)=y(t)−{right arrow over (x)}^(H)(t){right arrow over (w)}(t), e(t)is the error signal outputted by the AEC at the signal sample time t;y(t) is the signal received by the microphone at the signal sample timet; {right arrow over (w)}(t)=[w₀(t), w₁(t), . . . , w_(L-1)(t)]^(T),w_(t2)(t) is the (t2+1)-th coefficient of the FIR filter at the signalsample time t, t2=0, 1, 2, . . . , L−1; t is the digital-signal sampletime index number.

Further, when the processor executes the computer program, the processorfurther implements following steps:

according to an Equation:

{right arrow over (w)}(t+1)={right arrow over(w)}(t)+μ·X_(state)(t)[X_(state)^(H)(t)X_(state)(t)+δ^(opt)(t)·I_(P×P)]⁻¹·{right arrow over (e)}*(t),applying an affine projection (AP) algorithm to sustainably adaptivelyupdate the coefficient vector of the FIR filter;

where {right arrow over (w)}(t+1) is the coefficient vector of the FIRfilter after the coefficient vector of the FIR filter is updated; {rightarrow over (w)}(t) is the coefficient vector of the FIR filter beforethe coefficient vector of the FIR filter is updated; μ is apredetermined coefficient updating step-size parameter, and 0<μ<2;δ^(opt)(t) is a time-varying regularization factor; X_(state)(t) isL×P-dimension state matrix, and X_(state)(t)=[{right arrow over (x)}(t),{right arrow over (x)}(t−1), . . . , {right arrow over (x)}(t−P+1)];{right arrow over (x)}(t−t3) is the far-end reference signal vector atthe signal sample time (t−t3), and t3=0, 1, . . . , P−1, P is the orderquantity of the AP algorithm; X_(state) ^(H)(t) is the conjugatetranspose matrix of X_(state)(t); I_(P×P) is a P×P-dimension unitmatrix; {right arrow over (e)}*(t) is a complex conjugate of {rightarrow over (e)}(t), and {right arrow over (e)}(t)={right arrow over(y)}(t)−X_(state) ^(H)(t){right arrow over (w)}(t); {right arrow over(e)}(t) is a P-dimension error vector; {right arrow over (y)}(t) is aP-dimension vector of the signal received by the microphone, and {rightarrow over (y)}(t)=[y(t), y(t−1), . . . , y(t−P+1)]^(T); y(t−t3) is thesignal received by the microphone at the signal sample time (t−t3);{right arrow over (w)}(t)=[w₀(t), w₁(t), . . . , w_(L-1)(t)]^(T),w_(t2)(t) is the (t2+1)-th coefficient of the FIR filter at the signalsample time t, t2=0, 1, 2, . . . , L−1; t is a digital-signal sampletime index number.

Specifically, the preset signal includes a subband spectrum of thenear-end speech signal received by the microphone and inputted in theAEC and a subband spectrum of a far-end reference speech signal. Thecoefficient vector of the FIR filter is a subband-domain coefficientvector of the FIR filter and the time-varying regularization factor is asubband-domain time-varying regularization factor. When the processorexecutes the computer program, the processor further implementsfollowing steps: obtaining a subband power spectrum of the signalreceived by the microphone and an effective estimation value of asubband-domain coupling factor, respectively; according to the subbandpower spectrum of the signal received by the microphone and theeffective estimation value of the subband-domain coupling factor,obtaining a subband-domain time-varying regularization factor used foriteratively updating the subband-domain coefficient vector of the FIRfilter when the subband-domain coefficient vector of the FIR filter isused for processing a preset signal.

Further, when the processor executes the computer program, the processorfurther implements following steps:

according to an Equation:

${{\hat{\sigma}}_{Y}^{2}\left( {k,n} \right)} = \left\{ {\begin{matrix}{{{\alpha_{a} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}} + {\left( {1 - \alpha_{a}} \right) \cdot {{Y\left( {k,n} \right)}}^{2}}},{{{if}\mspace{14mu}{{Y\left( {k,n} \right)}}^{2}} > {{\hat{\sigma}}_{Y}^{2}\ \left( {k,{n - 1}} \right)}}} \\{{{\alpha_{d} \cdot {{\hat{\sigma}}_{Y}^{2}\ \left( {k,{n - 1}} \right)}} + {\left( {1 - \alpha_{d}} \right) \cdot {{Y\left( {k,n} \right)}}^{2}}},{{{if}\mspace{14mu}{{Y\left( {k,n} \right)}}^{2}} \leq {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}}}\end{matrix},} \right.$obtaining the subband power spectrum of the signal received by themicrophone;

where {circumflex over (σ)}_(Y) ²(k, n) is the subband power spectrum ofthe signal received by the microphone; Y(k, n) is a subband spectrum ofthe signal received by the microphone; α_(a) and α_(d) are presetrecursive constant quantities, and 0≤α_(a)<α_(d)<1; k is a subband indexvariable, k=0, 1, 2, . . . , K−1, and K is the total number of subbands;n is the signal frame time index variable.

Further, when the processor executes the computer program, the processorfurther implements following steps:

obtaining a biased estimation value of the subband-domain couplingfactor according to a cross-correlation method; obtaining a correctionfactor used for compensating for the biased estimation value of thesubband-domain coupling factor; obtaining an effective estimation valueof the subband-domain coupling factor according to the biased estimationvalue of the subband-domain coupling factor and the correction factor.

Further, when the processor executes the computer program, the processorfurther implements following steps:

according to an Equation:

${\left. {\hat{\beta}\left( {k,n} \right)} \right|_{{Cross}\text{-}{correlation}} = \frac{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{E\left( {k,{n - {n\; 1}}} \right)}{X^{*}\left( {k,{n - {n\; 1}}} \right)}}}}^{2}}{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{X\left( {k,{n - {n\; 1}}} \right)}}^{2}}}^{2}}},$obtaining the biased estimation value of the subband-domain couplingfactor;

where {circumflex over (β)}(k, n)|_(Cross-correlation) is a biasedestimation value of the subband-domain coupling factor; X*(k, n−n1) is acomplex conjugate of X(k, n−n1); n1=0, 1, 2, . . . , N_(s)−1, N_(s) isthe number of signal frames used for estimation of {circumflex over(β)}(k, n)|_(Cross-correlation), and N_(s)<<L_(s), L_(s) is the numberof coefficients of the FIR filter in each subband; E(k, n−n) is asubband spectrum of an error signal outputted by the AEC at signal frametime (n−n1); E(k, n)=Y(k, n)−{right arrow over (X)}_(k) ^(H)(n){rightarrow over (W)}_(k)(n), E(k, n) is the subband spectrum of the errorsignal outputted by the AEC at signal frame time n; Y(k, n) is thesubband spectrum of the signal received by the microphone at the signalframe time n; {right arrow over (X)}_(k) ^(H)(n) is a conjugatetranspose matrix of {right arrow over (X)}_(k)(n); {right arrow over(X)}_(k)(n) is a subband-spectrum vector of the far-end referencesignal, and {right arrow over (X)}_(k)(n)=[X(k, n), X(k, n−1), . . . ,X(k, n−L_(s)+1)]^(T); X(k, n−n2) is a subband spectrum of a far-endreference signal at signal frame time (n−n2); T is a transpose operator;{right arrow over (W)}_(k)(n) is the coefficient vector of the FIRfilter in a subband k, {right arrow over (W)}_(k)(n)=[W₀(k, n), W₁(k,n), . . . , W_(L) _(s) ₋₁(k, n)]^(T); W_(n2)(k, n) is the (n2+1)-thcoefficient of the FIR filter in the subband k at the signal frame timen, n2=0, 1, 2, . . . , L_(s)−1; k is a subband index variable, k=0, 1,2, . . . , K−1, and K is the total number of subbands; n is the signalframe time index variable.

Further, when the processor executes the computer program, the processorfurther implements following steps:

obtaining a candidate value of a square of a magnitude of a correlationcoefficient between a subband spectrum of the error signal outputted bythe AEC and a subband spectrum of a far-end reference signal; obtaininga square of an effective magnitude of a correlation coefficient betweena subband spectrum of the error signal outputted by the AEC and asubband spectrum of the far-end reference signal according to thecandidate value of the square of the magnitude of the correlationcoefficient between the subband spectrum of the error signal outputtedby the AEC and the subband spectrum of the far-end reference signal, andtaking the square of the effective magnitude of the correlationcoefficient between the subband spectrum of the error signal outputtedby the AEC and the subband spectrum of the far-end reference signal asthe correction factor used for compensating for the biased estimationvalue of the subband-domain coupling factor.

Further, when the processor executes the computer program, the processorfurther implements following steps:

according to an Equation:

${{\hat{r}\left( {k,n} \right)} = \frac{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{E\left( {k,{n - {n\; 1}}} \right)}{X^{*}\left( {k,{n - {n\; 1}}} \right)}}}}^{2}}{\left( {\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{E\left( {k,{n - {n\; 1}}} \right)}}^{2}} \right)\left( {\sum\limits_{m = 0}^{N_{s} - 1}{{X\left( {k,{n - {n\; 1}}} \right)}}^{2}} \right)}},$obtaining the candidate value of the square of the magnitude of thecorrelation coefficient between the subband spectrum of the error signaloutputted by the AEC and the subband spectrum of the far-end referencesignal;

where {circumflex over (r)}(k, n) is the candidate value of the squareof the magnitude of the correlation coefficient between the subbandspectrum of the error signal outputted by the AEC and the subbandspectrum of the far-end reference signal; X*(k, n−n1) is a complexconjugate of X(k, n−n1); n1=0, 1, 2, . . . , N_(s)−1, N_(s) is thenumber of frames used for estimation of {circumflex over (r)}(k, n), andN_(s)<<L_(s), L_(s) is the number of coefficients of the FIR filter ineach subband; E(k, n−n1) is the subband spectrum of the error signaloutputted by the AEC at signal frame time (n−n1); E(k, n)=Y(k, n)−{rightarrow over (X)}_(k) ^(H)(n){right arrow over (W)}_(k)(n), E(k, n) is thesubband spectrum of the error signal outputted by the AEC at signalframe time n; Y(k, n) is a subband spectrum of a signal received by themicrophone; {right arrow over (X)}_(k) ^(H)(n) is a conjugate transposematrix of {right arrow over (X)}_(k)(n); {right arrow over (X)}_(k)(n)is the subband spectrum vector of the far-end reference signal, and{right arrow over (X)}_(k)(n)=[X(k, n), X(k, n−1), . . . , X(k,n−L_(s)+1)]^(T); X(k, n−n2) is the subband spectrum of the far-endreference signal at the signal frame time (n−n2); T is a transposeoperator; {right arrow over (W)}_(k)(n) is the coefficient vector of theFIR filter in a subband k; {right arrow over (W)}_(k)(n)=[W₀(k, n),W₁(k, n), . . . , W_(L) _(s) ₋₁(k, n)]^(T); W_(n2)(k, n) is a (n2+1)-thcoefficient of the FIR filter in the signal frame time n in the subbandk; n2=0, 1, 2, . . . , L_(s)−1; k is a subband index variable; k=, 1, 2,. . . , K−1, and K is the total number of subbands; n is the signalframe time index variable.

Further, when the processor executes the computer program, the processorfurther implements following steps:

according to an Equation

${{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2} = \left\{ {\begin{matrix}{{\hat{r}\left( {k,n} \right)},} & {{{if}\mspace{14mu}{\hat{r}\left( {k,n} \right)}} > {{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2}} \\{{{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2},} & {{{if}\mspace{14mu}{\hat{r}\left( {k,n} \right)}} \leq {{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2}}\end{matrix},} \right.$obtaining the square of the effective magnitude of the correlationcoefficient between the subband spectrum of the error signal outputtedby the AEC and the subband spectrum of the far-end reference signal;

where |{circumflex over (r)}_(EX)(k, n)|² is the square of the effectivemagnitude of the correlation coefficient between the subband spectrum ofthe error signal outputted by the AEC and the subband spectrum of thefar-end reference signal; {circumflex over (r)}(k, n) is the candidatevalue of the square of the magnitude of the correlation coefficientbetween the subband spectrum of the error signal outputted by the AECand the subband spectrum of the far-end reference signal; n is thesignal frame time index variable.

Further, when the processor executes the computer program, the processorfurther implements following steps:

according to an Equation:

${{\hat{\beta}\left( {k,n} \right)} = \frac{\left. {\hat{\beta}\left( {k,n} \right)} \right|_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2}}},$obtaining the effective estimation value of the subband-domain couplingfactor;

where {circumflex over (β)}(k, n) is the effective estimation value ofthe subband-domain coupling factor, {circumflex over (β)}(k,n)|_(Cross-correlation) is the biased estimation value of thesubband-domain coupling factor; |{circumflex over (r)}_(EX)(k, n)|² isthe square of the effective magnitude of the correlation coefficientbetween the subband spectrum of the error signal outputted by the AECand the subband spectrum of the far-end reference signal; n is thesignal frame time index variable.

Further, when the processor executes the computer program, the processorfurther implements following steps:

according to the Equation:

${{\delta^{opt}\left( {k,n} \right)} = {{\max\left\{ {\frac{L_{s} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,n} \right)}}{{\overset{\hat{}}{\beta}\left( {k,n} \right)} + \rho_{0}},\delta_{\min}} \right\}} = {\max\left\{ {\frac{{L_{s} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,n} \right)}}{{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2}}{\left. {\overset{\hat{}}{\beta}\left( {k,n} \right)} \middle| {}_{{Cross}\text{-}{correlation}}{+ \rho} \right.},\delta_{\min}} \right\}}}},$obtaining a subband-domain time-varying regularization factor used foriteratively updating the subband-domain coefficient vector of the FIRfilter when the subband-domain coefficient vector of the FIR filter isused for processing a preset signal;

where δ^(opt)(k, n) is the subband-domain time-varying regularizationfactor; {circumflex over (σ)}_(Y) ²(k, n) is the subband power spectrumof the signal received by the microphone;

${\hat{\beta}\left( {k,n} \right)} = \frac{\left. {\hat{\beta}\left( {k,n} \right)} \right|_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2}}$is the effective estimation value of the subband-domain coupling factor,{circumflex over (β)}(k, n)|_(Cross-correlation) is the biasedestimation value of the subband-domain coupling factor; |{circumflexover (r)}_(EX)(k, n)|² is the square of the effective magnitude of thecorrelation coefficient between the subband spectrum of the error signaloutputted by the AEC and the subband spectrum of the far-end referencesignal; δ_(min) is the preset small real constant quantity, andδ_(min)>0; ρ₀ and ρ are preset small real constants, and ρ>0, ρ₀>0; n isthe signal frame time index variable.

Further, when the processor executes the computer program, the processorfurther implements following steps:

according to an Equation:

{right arrow over (W)}_(k)(n+1)={right arrow over (W)}_(k)(n)+μ·{rightarrow over (X)}_(k)(n)E*(k, n)/[{right arrow over (X)}_(k) ^(H)(n){rightarrow over (X)}_(k)(n)+δ^(opt)(k, n)], sustainably adaptively updatingthe subband-domain coefficient vector of the FIR filter using aNormalized Least Mean Square (NLMS) algorithm;

where {right arrow over (W)}_(k)(n+1) is the coefficient vector of theFIR filter in the subband k after the coefficient vector of the FIRfilter is updated; {right arrow over (W)}_(k)(n) is the coefficientvector of the FIR filter in the subband k before the coefficient vectorof the FIR filter is updated; μ is a predetermined coefficient updatingstep-size parameter, and 0<μ<2; {right arrow over (X)}_(k)(n) is thesubband spectrum vector of the far-end reference signal; {right arrowover (X)}_(k)(n)=[X(k, n), X(k, n−1), . . . , X(k, n−L_(s)+1)]^(T); X(k,n−n2) is the subband spectrum of the far-end reference signal at thesignal frame time (n−n2); n2=0, 1, . . . , L_(s)−1, L_(s) is the numberof coefficients of the FIR filter in each subband, T is the transposeoperator; {right arrow over (X)}_(k) ^(H)(n) is a conjugate transposematrix of {right arrow over (X)}_(k)(n); E*(k, n) is the complexconjugate of E(k, n); E(k, n) is the subband spectrum of the errorsignal outputted by AEC at the signal frame time “n”, and E(k, n)=Y(k,n)−{right arrow over (X)}_(k) ^(H)(n){right arrow over (W)}_(k)(n); Y(k,n) is the subband spectrum of the signal received by the microphone atthe signal frame time “n”; {right arrow over (W)}_(k)(n)=[W₀(k, n),W₁(k, n), . . . , W_(L) _(s) ₋₁(k, n)]^(T); W_(n2)(k, n) is the(n2+1)-th coefficient of the FIR filter in the subband k at the signalframe time “n”; δ^(opt)(k, n) is the subband-domain time-varyingregularization factor; k is a subband index variable, k=, 1, 2, . . . ,K−1, and K is the total number of subbands; n is the signal frame timeindex variable.

Further, when the processor executes the computer program, the processorfurther implements following steps:

according to an Equation:

{right arrow over (W)}_(k)(n+1)={right arrow over(W)}_(k)(n))+μ·X_(state)(k, n)·[X_(state) ^(H)(k, n)X_(state)(k,n)+δ^(opt)(k, n)·I_(P×P)]⁻¹·{right arrow over (E)}_(k)*(n), substainablyadaptively updating the subband-domain coefficient vector of the FIRfilter using an affine projection (AP) algorithm;

where {right arrow over (W)}_(k)(n+1) is the coefficient vector of theFIR filter in the subband k after the coefficient vector of the FIRfilter is updated; {right arrow over (W)}_(k)(n) is the coefficientvector of the FIR filter in the subband k before the coefficient vectorof the FIR filter is updated; μ is a predetermined coefficient updatingstep-size parameter, and 0<μ<2; δ^(opt)(k, n) is the subband-domaintime-varying regularization factor; X_(state)(k, n) is an L×P-dimensionstate matrix in the subband k, and X_(state)(k, n)=[{right arrow over(X)}_(k)(n), {right arrow over (X)}_(k)(n−1), . . . , {right arrow over(X)}_(k)(n−P+1)] is the subband spectrum vector of the far-end referencesignal at the signal frame time (n−n3), and n3=0, 1, . . . , P−1, P isthe order quantity of the AP algorithm; X_(state) ^(H)(k, n) is theconjugate transpose matrix of X_(state)(k, n); I_(P×P) is aP×P-dimension unit matrix; {right arrow over (E)}_(k)*(n) is the complexconjugate of {right arrow over (E)}_(k)(n), and {right arrow over(E)}_(k)(n)={right arrow over (Y)}_(k)(n)−X_(state) ^(H)(k, n){rightarrow over (W)}_(k)(n); {right arrow over (E)}_(k)(n) is the subbandspectrum vector of a P-dimensional error signal; {right arrow over(Y)}_(k)(n) is the P-dimension subband spectrum vector of the signalreceived by the microphone, and {right arrow over (Y)}_(k)(n)=[Y(k, n),Y(k, n−1), . . . , Y(k, n−P+1)]^(T); Y(k, n−n3) is the signal receivedby the microphone at the signal frame time (n−n3); {right arrow over(W)}_(k)(n)=[W₀(k, n), W₁(k, n), . . . , W_(L) _(s) ₋₁ (k, n)]^(T);W_(n2)(k, n) is the (n2+1)-th coefficient of the FIR filter in thesubband k at the signal frame time “n”, n2=0, 1, . . . , L_(s)−1, L_(s)is the number of coefficients of the FIR filter in each subband; k is asubband index variable, k=0, 1, 2, . . . , K−1, and K is the totalnumber of subbands; n is the signal frame time index variable.

The computer-readable storage medium mentioned in the present disclosuremay be a volatile medium or a non-volatile medium, a transient medium ora non-transient medium.

What has been described above are optional embodiments of the presentdisclosure. It should be noted that improvements and refinements may bemade by those of ordinary skills in the art without departing from theprinciple described herein. Such improvements and refinements are alsowithin the scope of the present disclosure.

What is claimed is:
 1. A sustainable adaptive updating method of acoefficient vector of a Finite Impulse Response (FIR) filter,comprising: obtaining a time-varying regularization factor used foriteratively updating the coefficient vector of the FIR filter in a casethat the coefficient vector of the FIR filter is used for processing apreset signal; updating the coefficient vector of the FIR filteraccording to the time-varying regularization factor; wherein the presetsignal comprises following combined pair: a far-end reference speechsignal inputted in an Acoustic Echo Canceller (AEC) and a near-endspeech signal received by a microphone, obtaining the time-varyingregularization factor used for iteratively updating the coefficientvector of the FIR filter in a case that the coefficient vector of theFIR filter is used for processing the preset signal, comprises:obtaining a power of a near-end signal received by the microphone and aneffective estimation value of a coupling factor; according to the powerof the near-end signal received by the microphone and the effectiveestimation value of the coupling factor, obtaining the time-varyingregularization factor used for iteratively updating the coefficientvector of the FIR filter in a case that the coefficient vector of theFIR filter is used for processing the preset signal; according to thepower of the near-end signal received by the microphone and theeffective estimation value of the coupling factor, obtaining thetime-varying regularization factor used for iteratively updating thecoefficient vector of the FIR filter in a case that the coefficientvector of the FIR filter is used for processing the preset signal,comprises: according to an Equation:${{\delta^{opt}(t)} = {{\max\left\{ {\frac{L \cdot {{\overset{\hat{}}{\sigma}}_{y}^{2}(t)}}{{\overset{\hat{}}{\beta}(t)} + \rho_{0}},\ \delta_{\min}} \right\}} = {\max\left\{ {\frac{{L \cdot {{\overset{\hat{}}{\sigma}}_{y}^{2}(t)}}{{{\overset{\hat{}}{r}}_{ex}(t)}}^{2}}{\left. {\overset{\hat{}}{\beta}(t)} \middle| {}_{{Cross} - {correlation}}{+ \rho} \right.},\ \delta_{\min}} \right\}}}},$obtaining the time-varying regularization factor used for iterativelyupdating the coefficient vector of the FIR filter; wherein δ^(opt)(t) isthe time-varying regularization factor; L is a quantity of coefficientsof the FIR filter; {circumflex over (σ)}_(y) ²(t) is the power of thesignal received by the microphone;${\overset{\hat{}}{\beta}(t)} = \frac{\left. {\overset{\hat{}}{\beta}(t)} \right|_{{Cross}\mspace{14mu}{correlatio}n}}{{{{\overset{\hat{}}{r}}_{ex}(t)}}^{2}}$is the effective estimation value of coupling factor; {circumflex over(β)}(t)|_(Cross-correlation) is a biased estimation value of thecoupling factor based on a cross-correlation technique; |{circumflexover (r)}_(ex)(t)|² is a square of an effective magnitude of acorrelation coefficient between an error signal outputted by the AEC andthe far-end reference signal; δ_(min) is a preset small real constantquantity, and δ_(min)>0; ρ₀ and ρ are preset small real constants, andρ>0, ρ₀>0, respectively; t is a digital-signal sample time index number;or, wherein the preset signal comprises a subband spectrum of a near-endspeech signal received by a microphone and a subband spectrum of afar-end reference speech signal inputted in an AEC; the coefficientvector of the FIR filter is a subband-domain coefficient vector of theFIR filter and the time-varying regularization factor is asubband-domain time-varying regularization factor; obtaining thetime-varying regularization factor used for iteratively updating thecoefficient vector of the FIR filter in a case that the coefficientvector of the FIR filter is used for processing the preset signal,comprises: obtaining a subband power spectrum of the near-end signalreceived by the microphone and an effective estimation value of asubband-domain coupling factor, respectively; according to the subbandpower spectrum of the near-end signal received by the microphone and theeffective estimation value of the subband-domain coupling factor,obtaining a subband-domain time-varying regularization factor used foriteratively updating the subband-domain coefficient vector of the FIRfilter in a case that the subband-domain coefficient vector of the FIRfilter is used for processing a preset signal; wherein according to thesubband power spectrum of the near-end signal received by the microphoneand the effective estimation value of the subband-domain couplingfactor, obtaining the subband-domain time-varying regularization factorused for iteratively updating the subband-domain coefficient vector ofthe FIR filter in a case that the subband-domain coefficient vector ofthe FIR filter is used for processing the preset signal, comprises:according to an Equation:${{\delta^{opt}\left( {k,n} \right)} = {{\max\left\{ {\frac{L_{s} \cdot {{\overset{\hat{}}{\sigma}}_{Y}^{2}\left( {k,n} \right)}}{{\overset{\hat{}}{\beta}\left( {k,n} \right)} + \rho_{0}},\ \delta_{\min}} \right\}} = {\max\left\{ {\frac{{L_{s} \cdot {{\overset{\hat{}}{\sigma}}_{Y}^{2}\left( {k,n} \right)}}{{{\overset{\hat{}}{r}}_{EX}\left( {k,n} \right)}}^{2}}{\left. {\overset{\hat{}}{\beta}\left( {k,n} \right)} \middle| {}_{{Cross} - {correlation}}{+ \rho} \right.},\ \delta_{\min}} \right\}}}},$obtaining the subband-domain time-varying regularization factor used foriteratively updating the subband-domain coefficient vector of the FIRfilter in a case that the subband-domain coefficient vector of the FIRfilter is used for processing the preset signal; wherein δ^(opt)(k, n)is the subband-domain time-varying regularization factor; {circumflexover (σ)}_(y) ²(k, n) is a subband power spectrum of a near-end signalreceived by the microphone;${\overset{\hat{}}{\beta}\left( {k,n} \right)} = \frac{\left. {\overset{\hat{}}{\beta}\left( {k,n} \right)} \right|_{{Cross} - {correlation}}}{{{{\overset{\hat{}}{r}}_{EX}\left( {k,n} \right)}}^{2}}$is the effective estimation value of the subband-domain coupling factor;{circumflex over (β)}(k, n)|_(Cross-correlation) is the biasedestimation value of the subband-domain coupling factor; |{circumflexover (r)}_(EX)(k, n)|² is the square of the effective magnitude of thecorrelation coefficient between the subband spectrum of the error signaloutputted by the AEC and the subband spectrum of the far-end referencespeech signal; δ_(min) is a preset small real constant quantity, andδ_(min)>0; ρ₀ and ρ are preset small real constants, and ρ>0, ρ₀>0, n isa signal frame time index variable.
 2. The sustainable adaptive updatingmethod according to claim 1, wherein a manner of obtaining the power ofthe near-end signal received by the microphone is: according to anEquation: ${{\hat{\sigma}}_{y}^{2}(t)} = \left\{ \begin{matrix}{{{\alpha_{a} \cdot {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}} + {\left( {1 - \alpha_{a}} \right) \cdot {{y(t)}}^{2}}},{{{if}\mspace{14mu}{{y(t)}}^{2}} > {{\hat{\sigma}}_{y}^{2}\ \left( {t - 1} \right)}}} \\{{{\alpha_{d} \cdot {{\hat{\sigma}}_{y}^{2}\ \left( {t - 1} \right)}} + {\left( {1 - \alpha_{d}} \right) \cdot {{y(t)}}^{2}}},{{{if}\mspace{14mu}{{y(t)}}^{2}}\  \leq {{\hat{\sigma}}_{y}^{2}\left( {t - 1} \right)}}}\end{matrix} \right.$ obtaining the power of the near-end signalreceived by the microphone; wherein {circumflex over (σ)}_(y) ²(t) isthe power of the near-end signal received by the microphone; y(t) is thenear-end signal received by the microphone; α_(a) and α_(d) are presetrecursive constant quantities, and 0≤α_(a)<α_(d)<1; t is adigital-signal time index number.
 3. The sustainable adaptive updatingmethod according to claim 1, wherein a manner of obtaining the effectiveestimation value of the coupling factor is: obtaining a biasedestimation value of the coupling factor according to a cross-correlationmethod; obtaining a correction factor used for compensating for thebiased estimation value of the coupling factor; obtaining the effectiveestimation value of the coupling factor according to the biasedestimation value of the coupling factor and the correction factor. 4.The sustainable adaptive updating method according to claim 3, wherein,obtaining the biased estimation value of the coupling factor accordingto the cross-correlation method, comprises: according to an Equation:${\left. {\hat{\beta}(t)} \right|_{{Cross}\text{-}{correlation}} = \frac{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{e\left( {t - {t\; 1}} \right)}{x^{*}\left( {t - {t\; 1}} \right)}}}}^{2}}{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{x\left( {t - {t\; 1}} \right)}}^{2}}}^{2}}},$obtaining the biased estimation value of the coupling factor, wherein{circumflex over (β)}(t)|_(Cross-correlation) is the biased estimationvalue of the coupling factor based on a cross-correlation technique;x*(t−t1) is a complex conjugate of x(t−t1); t1=0, 1, 2, . . . , T_(s)−1,T_(s) is a quantity of samples used in estimation of the {circumflexover (β)}(t)|_(Cross-correlation), and T_(s)<<L, L is a quantity ofcoefficients of the FIR filter; e(t−t1) is an error signal outputted bythe AEC at a signal sample time (t−t1), e(t)=y(t)−{right arrow over(x)}^(H)(t){right arrow over (w)}(t), e(t) is an error signal outputtedby the AEC at a signal sample time t; y(t) is a near-end signal receivedby the microphone at a signal sample time t; {right arrow over(x)}^(H)(t) is a conjugate transpose matrix of {right arrow over(x)}(t); {right arrow over (x)}(t) is a far-end reference speech signalvector and {right arrow over (x)}(t)=[x(t), x(t−1), . . . ,x(t−L+1)]^(T); x(t−t2) is a far-end reference speech signal at a signalsample time (t−t2); T is a transpose operator; {right arrow over (w)}(t)is the coefficient vector of the FIR filter, {right arrow over(w)}(t)=[w₀(t), w₁(t), . . . , w_(L-1)(t)]^(T), w_(t2)(t) is a (t2+1)-thcoefficient of the FIR filter at the signal sample time t, t2=0, 1, 2, .. . , L−1; t is a digital-signal time index number; and/or, obtainingthe effective estimation value of the coupling factor according to thebiased estimation value of the coupling factor and the correctionfactor, comprises: obtaining the effective estimation value of thecoupling factor according to the Equation:${{\hat{\beta}(t)} = \frac{\left. {\hat{\beta}(t)} \right|_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{ex}(t)}}^{2}}},$wherein {circumflex over (β)}(t)) is the effective estimation value ofthe coupling factor; {circumflex over (β)}(t)|_(Cross-correlation) isthe biased estimation value of the coupling factor based on across-correlation technique; |{circumflex over (r)}_(ex)(t)|² is thesquare of the effective magnitude of the correlation coefficient betweenthe error signal outputted by the AEC and the far-end reference speechsignal as the correction factor; t is a digital-signal sample time indexnumber.
 5. The sustainable adaptive updating method according to claim3, wherein obtaining the correction factor used for compensating for thebiased estimation value of the coupling factor, comprises: obtaining acandidate value of a square of a magnitude of a correlation coefficientbetween an error signal outputted by the AEC and a far-end referencespeech signal; obtaining, according to the candidate value of the squareof the magnitude of the correlation coefficient between the error signaloutputted by the AEC and the far-end reference speech signal, a squareof an effective magnitude of the correlation coefficient between theerror signal outputted by the AEC and the far-end reference speechsignal, and taking the square of the effective magnitude of thecorrelation coefficient between the error signal outputted by the AECand the far-end reference speech signal as the correction factor usedfor compensating for the biased estimation value of the coupling factor.6. The sustainable adaptive updating method according to claim 5,wherein, obtaining the candidate value of the square of the magnitude ofthe correlation coefficient between the error signal outputted by theAEC and the far-end reference speech signal, comprises: according to anEquation:${{\hat{r}(t)} = \frac{{{\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{e\left( {t - {t\; 1}} \right)}{x^{*}\left( {t - {t\; 1}} \right)}}}}^{2}}{\left( {\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{e\left( {t - {t\; 1}} \right)}}^{2}} \right)\left( {\sum\limits_{{t\; 1} = 0}^{T_{s} - 1}{{x\left( {t - {t\; 1}} \right)}}^{2}} \right)}},$obtaining the candidate value of the square of the magnitude of thecorrelation coefficient between the error signal outputted by the AECand the far-end reference speech signal; wherein {circumflex over(r)}(t) is the candidate value of the square of the magnitude of thecorrelation coefficient between the error signal outputted by the AECand the far-end reference speech signal; x(t−t1) is the far-endreference speech signal at a signal sample time (t−t1); x*(t−t1) is acomplex conjugate of x(t−t1); t1=0, 1, 2, . . . , T_(s)−1, T_(s) is aquantity of samples used in estimation of {circumflex over (r)}(t), andT_(s)<<L, L is a quantity of coefficients of the FIR filter; e(t−t1) isthe error signal outputted by the AEC at a signal sample time (t−t1),e(t)=y(t)−{right arrow over (x)}^(H)(t){right arrow over (w)}(t), e(t)is an error signal outputted by the AEC at a signal sample time t; y(t)is the near-end signal received by the microphone at the signal sampletime t; {right arrow over (x)}^(H)(t) is a conjugate transpose vector of{right arrow over (x)}(t); {right arrow over (x)}(t) is a far-endreference signal vector and {right arrow over (x)}(t)=[x(t), x(t−1), . .. , x(t−L+1)]^(T); x(t−t2) is the far-end reference speech signal at asignal sample time (t−t2); T is a transpose operator; {right arrow over(w)}(t) is the coefficient vector of the FIR filter, {right arrow over(w)}(t)=[w₀(t), w₁(t), . . . , w_(L-1)(t)]^(T), w_(t2)(t) (t) is a(t2+1)-th coefficient of the FIR filter at the signal sample time t,t2=0, 1, 2, . . . , L−1; t is a digital-signal sample time index number;and/or, obtaining, according to the candidate value of the square of themagnitude of the correlation coefficient between the error signaloutputted by the AEC and the far-end reference speech signal, the squareof the effective magnitude of the correlation coefficient between theerror signal outputted by the AEC and the far-end reference speechsignal, comprises: according to an Equation:${{{\hat{r}}_{ex}(t)}}^{2} = \left\{ \begin{matrix}{{\hat{r}(t)},} & {{{if}\mspace{14mu}{\hat{r}(t)}} > {{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2}} \\{{{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2},} & {{{if}\mspace{14mu}{\hat{r}(t)}} \leq {{{\hat{r}}_{ex}\left( {t - 1} \right)}}^{2}}\end{matrix} \right.$ obtaining the square of the effective magnitude ofthe correlation coefficient between the signal outputted by the AEC andthe far-end reference speech signal; wherein |{circumflex over(r)}_(ex)(t)|² is the square of the effective magnitude of thecorrelation coefficient between the error signal outputted by the AECand the far-end reference signal; {circumflex over (r)}(t) is thecandidate value of the square of the magnitude of the correlationcoefficient between the error signal outputted by the AEC and thefar-end reference speech signal; t is a digital-signal sample time indexnumber.
 7. The sustainable adaptive updating method according to claim1, wherein, updating the coefficient vector of the FIR filter accordingto the time-varying regularization factor, comprises: according to anEquation: {right arrow over (w)}(t+1)={right arrow over (w)}(t)+μ·{rightarrow over (x)}(t)e*(t)/[{right arrow over (x)}^(H)(t){right arrow over(x)}(t)+δ^(opt)(t)], sustainably adaptively updating the coefficientvector of the FIR filter by applying a Normalized Least Mean Square(NLMS) algorithm, wherein, {right arrow over (w)}(t+1) is thecoefficient vector of the FIR filter after the coefficient vector of theFIR filter is updated; {right arrow over (w)}(t) is the coefficientvector of the FIR filter before the coefficient vector of the FIR filteris updated; μ is a predetermined coefficient updating step-sizeparameter, and 0<μ<2; {right arrow over (x)}(t) is a far-end referencespeech signal vector and {right arrow over (x)}(t)=[x(t), x(t−1), . . ., x(t−L+1)]^(T); x(t−t2) is the far-end reference speech signal at asignal sample time (t−t2); T is a transpose operator; {right arrow over(x)}^(H)(t) is a conjugate transpose vector of {right arrow over(x)}(t); δ^(opt)(t) is the time-varying regularization factor; e*(t) isa complex conjugate of e(t); e(t)=y(t)−{right arrow over(x)}^(H)(t){right arrow over (w)}(t), e(t) is the error signal outputtedby the AEC at a signal sample time t; y(t) is the near-end signalreceived by the microphone at the signal sample time t; {right arrowover (w)}(t)=[w₀(t), w₁(t), . . . , w_(L-1)(t)]^(T), w_(t2)(t) is a(t2+1)-th coefficient of the FIR filter at the signal sample time t,t2=0, 1, 2, . . . , L−1; t is a digital-signal sample time index number;or, updating the coefficient vector of the FIR filter according to thetime-varying regularization factor, comprises: according to an Equation:{right arrow over (w)}(t+1)={right arrow over(w)}(t)+μ·X_(state)(t)[X_(state)^(H)(t)X_(state)(t)+δ^(opt)(t)·I_(P×P)]⁻¹·{right arrow over (e)}*(t)applying an affine projection (AP) algorithm to sustainably adaptivelyupdate the coefficient vector of the FIR filter; wherein {right arrowover (w)}(t+1) is the coefficient vector of the FIR filter after thecoefficient vector of the FIR filter is updated; {right arrow over(w)}(t) is the coefficient vector of the FIR filter before thecoefficient vector of the FIR filter is updated; μ is a predeterminedcoefficient updating step-size parameter, and 0<μ<2; δ^(opt)(t) is thetime-varying regularization factor; X_(state)(t) is L×P-dimension statematrix, and X_(state)(t)=[{right arrow over (x)}(t), {right arrow over(x)}(t−1), . . . , {right arrow over (x)}(t−P+1)]; {right arrow over(x)}(t−t3) is a far-end reference signal vector at a signal sample time(t−t3), and t3=0, 1, . . . , P−1, P is an order quantity of the APalgorithm; X_(state) ^(H)(t) is a conjugate transpose matrix ofX_(state)(t); I_(P×P) is a P×P-dimension unit matrix; {right arrow over(e)}*(t) is a complex conjugate of {right arrow over (e)}(t), and {rightarrow over (e)}(t)={right arrow over (y)}(t)−X_(state) ^(H)(t){rightarrow over (w)}(t); {right arrow over (e)}(t) is a P-dimension errorvector; {right arrow over (y)}(t) is a P-dimension vector of thenear-end signal received by the microphone, and {right arrow over(y)}(t)=[y(t), y(t−1), . . . , y(t−P+1)]^(T); y(t−t3) is the near-endsignal received by the microphone at a signal sample time (t−t3); {rightarrow over (w)}(t)=[w₀(t), w₁(t), . . . , w_(L-1)(t)]^(T), w_(t2)(t) isa (t2+1)-th coefficient of the FIR filter at a signal sample time t,t2=0, 1, 2, . . . , L−1; t is a digital-signal sample time index number.8. The sustainable adaptive updating method according to claim 1,wherein a manner of obtaining the subband power spectrum of the near-endsignal received by the microphone is: according to an Equation:${{\hat{\sigma}}_{Y}^{2}\left( {k,n} \right)} = \left\{ {\begin{matrix}{{{\alpha_{a} \cdot {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}} + {\left( {1 - \alpha_{a}} \right) \cdot {{Y\left( {k,n} \right)}}^{2}}},{{{if}\mspace{14mu}{{Y\left( {k,n} \right)}}^{2}} > {{\hat{\sigma}}_{Y}^{2}\ \left( {k,{n - 1}} \right)}}} \\{{{\alpha_{d} \cdot {{\hat{\sigma}}_{Y}^{2}\ \left( {k,{n - 1}} \right)}} + {\left( {1 - \alpha_{d}} \right) \cdot {{Y\left( {k,n} \right)}}^{2}}},{{{if}\mspace{14mu}{{Y\left( {k,n} \right)}}^{2}} \leq {{\hat{\sigma}}_{Y}^{2}\left( {k,{n - 1}} \right)}}}\end{matrix},} \right.$ obtaining the subband power spectrum of thenear-end signal received by the microphone; wherein {circumflex over(σ)}_(Y) ²(k, n) is the subband power spectrum of the near-end signalreceived by the microphone; Y(k, n) is a subband spectrum of thenear-end signal received by the microphone; α_(a) and α_(d) are presetrecursive constant quantities, and 0≤α_(a)<α_(d)<1; k is a subband indexvariable, k=0, 1, 2, . . . , K−1, and K is a total quantity of subbands;n is a signal frame time index variable.
 9. The sustainable adaptiveupdating method according to claim 1, wherein a manner of obtaining theeffective estimation value of the subband-domain coupling factor,comprises: obtaining a biased estimation value of the subband-domaincoupling factor according to a cross-correlation method; obtaining acorrection factor used for compensating for the biased estimation valueof the subband-domain coupling factor; obtaining the effectiveestimation value of the subband-domain coupling factor according to thebiased estimation value of the subband-domain coupling factor and thecorrection factor.
 10. The sustainable adaptive updating methodaccording to claim 9, wherein, obtaining the biased estimation value ofthe subband-domain coupling factor according to the cross-correlationmethod, comprises: according to an Equation:${\left. {\hat{\beta}\left( {k,n} \right)} \right|_{{Cross}\text{-}{correlation}} = \frac{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{E\left( {k,{n - {n\; 1}}} \right)}{X^{*}\left( {k,{n - {n\; 1}}} \right)}}}}^{2}}{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{X\left( {k,{n - {n\; 1}}} \right)}}^{2}}}^{2}}},$obtaining the biased estimation value of the subband-domain couplingfactor; wherein {circumflex over (β)}(k, n)|_(Cross-correlation) is thebiased estimation value of the subband-domain coupling factor; X*(k,n−n1) is a complex conjugate of X(k, n−n1); n1=0, 1, 2, . . . , N_(s)−1,N_(s) is a quantity of signal frames used in estimation of {circumflexover (β)}(k, n)|_(Cross-correlation), and N_(s)<<L_(s), L_(s) is aquantity of coefficients of the FIR filter in each subband; E(k, n−n1)is a subband spectrum of an error signal outputted by the AEC at asignal frame time (n−n1); E(k, n)=Y(k, n)−{right arrow over (X)}_(k)^(H)(n){right arrow over (W)}_(k)(n), E(k, n) is the subband spectrum ofthe error signal outputted by the AEC at a signal frame time n; Y(k, n)is the subband spectrum of the near-end signal received by themicrophone at the signal frame time n; {right arrow over (X)}_(k)^(H)(n) is a conjugate transpose vector of {right arrow over(X)}_(k)(n); {right arrow over (X)}_(k)(n) is a subband-spectrum vectorof the far-end reference signal, and {right arrow over (X)}_(k)(n)=[X(k,n), X(k, n−1), . . . , X(k, n−L_(s)+1)]^(T); X(k, n−n2) is a subbandspectrum of a far-end reference speech signal at a signal frame time(n−n2); T is a transpose operator; {right arrow over (W)}_(k)(n) is thecoefficient vector of the FIR filter in a subband k, {right arrow over(W)}_(k)(n)=[W₀(k, n), W₁(k, n), . . . , W_(L) _(s) ₋₁(k, n)]^(T);W_(n2)(k, n) is a (n2+1)-th coefficient of the FIR filter in the subbandk at the signal frame time n, n2=0, 1, 2, . . . , L_(s)−1; k is asubband index variable, k=0, 1, 2, . . . , K−1, and K is a totalquantity of subbands; n is a signal frame time index variable; and/or,obtaining the effective estimation value of the subband-domain couplingfactor according to the biased estimation value of the subband-domaincoupling factor and the correction factor, comprises: according to anEquation:${{\hat{\beta}\left( {k,n} \right)} = \frac{\left. {\hat{\beta}\left( {k,n} \right)} \right|_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2}}},$obtaining the effective estimation value of the subband-domain couplingfactor; wherein {circumflex over (β)}(k, n) is the effective estimationvalue of the subband-domain coupling factor, {circumflex over (β)}(k,n)|_(Cross-correlation) is the biased estimation value of thesubband-domain coupling factor; |{circumflex over (r)}_(EX)(k, n)|² isthe square of the effective magnitude of the correlation coefficientbetween the subband spectrum of the error signal outputted by the AECand the subband spectrum of the far-end reference speech signal as thecorrection factor; n is a signal frame time index variable.
 11. Thesustainable adaptive update method according to claim 9, whereinobtaining the correction factor used for compensating for the biasedestimation value of the subband-domain coupling factor, comprises:obtaining a candidate value of a square of a magnitude of a correlationcoefficient between a subband spectrum of an error signal outputted byan AEC and a subband spectrum of a far-end reference speech signal;obtaining, according to the candidate value of the square of themagnitude of the correlation coefficient between the subband spectrum ofthe error signal outputted by the AEC and the subband spectrum of thefar-end reference speech signal, a square of an effective magnitude ofthe correlation coefficient between the subband spectrum of the errorsignal outputted by the AEC and the subband spectrum of the far-endreference speech signal, and taking the square of the effectivemagnitude of the correlation coefficient between the subband spectrum ofthe error signal outputted by the AEC and the subband spectrum of thefar-end reference speech signal as the correction factor used forcompensating for the biased estimation value of the subband-domaincoupling factor.
 12. The sustainable adaptive updating method accordingto claim 11, wherein obtaining the candidate value of the square of themagnitude of the correlation coefficient between the subband spectrum ofthe error signal outputted by the AEC and the subband spectrum of thefar-end reference speech signal, comprises: according to an Equation:${{\hat{r}\left( {k,n} \right)} = \frac{{{\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{E\left( {k,{n - {n\; 1}}} \right)}{X^{*}\left( {k,{n - {n\; 1}}} \right)}}}}^{2}}{\left( {\sum\limits_{{n\; 1} = 0}^{N_{s} - 1}{{E\left( {k,{n - {n\; 1}}} \right)}}^{2}} \right)\left( {\sum\limits_{m = 0}^{N_{s} - 1}{{X\left( {k,{n - {n\; 1}}} \right)}}^{2}} \right)}},$obtaining the candidate value of the square of the magnitude of thecorrelation coefficient between the subband spectrum of the error signaloutputted by the AEC and the subband spectrum of the far-end referencespeech signal; wherein {circumflex over (r)}(k, n) is the candidatevalue of the square of the magnitude of the correlation coefficientbetween the subband spectrum of the error signal outputted by the AECand the subband spectrum of the far-end reference signal; X*(k, n−n1) isa complex conjugate of X(k, n−n1); n1=0, 1, 2, . . . , N_(s)−1, N_(s) isa quantity of frames used in estimation of {circumflex over (r)}(k, n),and N_(s)<<L_(s), L_(s) is a quantity of coefficients of the FIR filterin each subband; E(k, n−n1) is the subband spectrum of the error signaloutputted by the AEC at a signal frame time (n−n1); E(k, n)=Y(k,n)−{right arrow over (X)}_(k) ^(H)(n){right arrow over (W)}_(k)(n), E(k,n) is the subband spectrum of the error signal outputted by the AEC at asignal frame time n; Y(k, n) is a subband spectrum of a near-end signalreceived by the microphone; {right arrow over (X)}_(k) ^(H)(n) is aconjugate transpose vector of {right arrow over (X)}_(k)(n); {rightarrow over (X)}_(k)(n) is a subband spectrum vector of the far-endreference signal, and {right arrow over (X)}_(k)(n)=[X(k, n), X(k, n−1),. . . , X(k, n−L_(s)+1)]^(T); X(k, n−n2) is the subband spectrum of thefar-end reference speech signal at the signal frame time (n−n2); T is atranspose operator; {right arrow over (W)}_(k)(n) is the coefficientvector of the FIR filter in a subband k; {right arrow over(W)}_(k)(n)=[W₀(k, n), W₁(k, n), . . . , W_(L) _(s) ₋₁(k, n)]^(T);W_(n2)(k, n) is a (n2+1)-th coefficient of the FIR filter in the signalframe time n in the subband k; n2=0, 1, 2, . . . , L_(s)−1; k is asubband index variable; k=0, 1, 2, . . . , K−1, and K is a totalquantity of subbands; n is a signal frame time index variable.
 13. Thesustainable adaptive updating method according to claim 11, whereinobtaining, according to the candidate value of the square of themagnitude of the correlation coefficient between the subband spectrum ofthe error signal outputted by the AEC and the subband spectrum of thefar-end reference speech signal, the square of the effective magnitudeof the correlation coefficient between the subband spectrum of the errorsignal outputted by the AEC and the subband spectrum of the far-endreference speech signal, comprises: according to an Equation:${{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2} = \left\{ {\begin{matrix}{{\hat{r}\left( {k,n} \right)},} & {{{if}\mspace{14mu}{\hat{r}\left( {k,n} \right)}} > {{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2}} \\{{{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2},} & {{{if}\mspace{14mu}{\hat{r}\left( {k,n} \right)}} \leq {{{\hat{r}}_{EX}\left( {k,{n - 1}} \right)}}^{2}}\end{matrix},} \right.$ obtaining the square of the effective magnitudeof the correlation coefficient between the subband spectrum of the errorsignal outputted by the AEC and the subband spectrum of the far-endreference speech signal; wherein |{circumflex over (r)}_(EX)(k, n)|² isthe square of the effective magnitude of the correlation coefficientbetween the subband spectrum of the error signal outputted by the AECand the subband spectrum of the far-end reference signal; {circumflexover (r)}(k, n) is the candidate value of the square of the magnitude ofthe correlation coefficient between the subband spectrum of the errorsignal outputted by the AEC and the subband spectrum of the far-endreference speech signal; n is a signal frame time index variable. 14.The sustainable adaptive updating method according to claim 1, wherein,updating the coefficient vector of the FIR filter according to thetime-varying regularization factor, comprises: according to an Equation:{right arrow over (W)}_(k)(n+1)={right arrow over (W)}_(k)(n)+μ·{rightarrow over (X)}_(k)(n)E*(k, n)/[{right arrow over (X)}_(k) ^(H)(n){rightarrow over (X)}_(k)(n)+δ^(opt)(k, n)], sustainably adaptively updatingthe subband-domain coefficient vector of the FIR filter using aNormalized Least Mean Square (NLMS) algorithm; wherein {right arrow over(W)}_(k)(n+1) is a coefficient vector of the FIR filter in a subband kafter the coefficient vector of the FIR filter is updated; {right arrowover (W)}_(k)(n) is the coefficient vector of the FIR filter in thesubband k before the coefficient vector of the FIR filter is updated; μis a predetermined coefficient updating step-size parameter, and 0<μ<2;{right arrow over (X)}_(k)(n) is a subband spectrum vector of thefar-end reference signal; {right arrow over (X)}_(k)(n)=[X(k, n), X(k,n−1), . . . , X(k, n−L_(s)+1)]^(T); X(k, n−n2) is a subband spectrum ofthe far-end reference signal at a signal frame time (n−n2); n2=0, 1, . .. , L_(s)−1, L_(s) is a quantity of coefficients of the FIR filter ineach subband, T is a transpose operator; {right arrow over (X)}_(k)^(H)(n) is a conjugate transpose vector of {right arrow over(X)}_(k)(n); E*(k, n) is a complex conjugate of E(k, n); E(k, n) is thesubband spectrum of the error signal outputted by AEC at a signal frametime n, and E(k, n)=Y(k, n)−{right arrow over (X)}_(k) ^(H)(n){rightarrow over (W)}_(k)(n); Y(k, n) is the subband spectrum of the near-endsignal received by the microphone at the signal frame time n; {rightarrow over (W)}_(k)(n)=[W₀(k, n), W₁(k, n), . . . , W_(L) _(s) ₋₁(k,n)]^(T); W_(n2)(k, n) is a (n2+1)-th coefficient of the FIR filter inthe subband k at the signal frame time n; δ^(opt)(k, n) is thesubband-domain time-varying regularization factor; k is a subband indexvariable, k=0, 1, 2, . . . , K−1, and K is a total quantity of subbands;n is a signal frame time index variable; or, updating the coefficientvector of the FIR filter according to the time-varying regularizationfactor, comprises: according to an Equation: {right arrow over(W)}_(k)(n+1)={right arrow over (W)}_(k)(n)+μ·X_(state)(k, n)·[X_(state)^(H)(k, n)X_(state)(k, n)+δ^(opt)(k, n)·

_(P×P)]⁻¹·{right arrow over (E)}_(k)*(n), substainably adaptivelyupdating the subband-domain coefficient vector of the FIR filter usingan affine projection (AP) algorithm; wherein {right arrow over(W)}_(k)(n+1) is the coefficient vector of the FIR filter in a subband kafter the coefficient vector of the FIR filter is updated; {right arrowover (W)}_(k)(n) is the coefficient vector of the FIR filter in thesubband k before the coefficient vector of the FIR filter is updated; μis a predetermined coefficient updating step-size parameter, and 0<μ<2;δ^(opt)(k, n) is the subband-domain time-varying regularization factor;X_(state)(k, n) is an L×P-dimension state matrix in the subband k, andX_(state)(k, n)=[{right arrow over (X)}_(k)(n), {right arrow over(X)}_(k)(n−1), . . . , {right arrow over (X)}_(k)(n−P+1)]; {right arrowover (X)}_(k)(n−n3) is a subband spectrum vector of the far-endreference signal at a signal frame time (n−n3), and n3=0, 1, . . . ,P−1, P is an order quantity of the AP algorithm; X_(state) ^(H)(k, n) isa conjugate transpose matrix of X_(state)(k, n); I_(P×P) is aP×P-dimension unit matrix; {right arrow over (E)}_(k)*(n) is a complexconjugate of {right arrow over (E)}_(k)(n), and {right arrow over(E)}_(k)(n)={right arrow over (Y)}_(k)(n)−X_(state) ^(H)(k, n){rightarrow over (W)}_(k)(n); {right arrow over (E)}_(k)(n) is a P-dimensionsubband spectrum vector of an error signal; {right arrow over(Y)}_(k)(n) is a P-dimension subband spectrum vector of a near-endsignal received by the microphone, and {right arrow over(Y)}_(k)(n)=[Y(k, n), Y(k, n−1), . . . , Y(k, n−P+1)]^(T); Y(k, n−n3) isthe near-end signal received by the microphone at a signal frame time(n−n3); {right arrow over (W)}_(k)(n)=[W₀(k, n), W₁(k, n), . . . , W_(L)_(s) ₋₁(k, n)]^(T); W_(n2)(k, n) is a (n2+1)-th coefficient of the FIRfilter in the subband k at a signal frame time n, n2=0, 1, . . . ,L_(s)−1, L_(s) is a quantity of coefficients of the FIR filter in eachsubband; k is a subband index variable, k=0, 1, 2, . . . , K−1, and K isa total quantity of subbands; n is a signal frame time index variable.15. A sustainable adaptive updating device of a coefficient vector of aFinite Impulse Response (FIR) filter, comprising a storage, a processor,and a computer program stored on the storage and executable by theprocessor; wherein when the processor executes the computer program, theprocessor implements following steps: obtaining a time-varyingregularization factor used for iteratively updating the coefficientvector of the FIR filter in a case that the coefficient vector of theFIR filter is used for processing a preset signal; updating thecoefficient vector of the FIR filter according to the time-varyingregularization factor; wherein the preset signal comprises followingcombined pair: a far-end reference speech signal inputted in an AcousticEcho Canceller (AEC) and a near-end speech signal received by amicrophone, obtaining the time-varying regularization factor used foriteratively updating the coefficient vector of the FIR filter in a casethat the coefficient vector of the FIR filter is used for processing thepreset signal, comprises: obtaining a power of a near-end signalreceived by a microphone and an effective estimation value of a couplingfactor; according to the power of the near-end signal received by themicrophone and the effective estimation value of the coupling factor,obtaining the time-varying regularization factor used for iterativelyupdating the coefficient vector of the FIR filter in a case that thecoefficient vector of the FIR filter is used for processing the presetsignal; according to the power of the near-end signal received by themicrophone and the effective estimation value of the coupling factor,obtaining the time-varying regularization factor used for iterativelyupdating the coefficient vector of the FIR filter in a case that thecoefficient vector of the FIR filter is used for processing the presetsignal, comprises: according to an Equation:${{\delta^{opt}(t)} = {{\max\left\{ {\frac{L \cdot {{\hat{\sigma}}_{Y}^{2}(t)}}{{\overset{\hat{}}{\beta}(t)} + \rho_{0}},\delta_{\min}} \right\}} = {\max\left\{ {\frac{{L \cdot {{\hat{\sigma}}_{Y}^{2}(t)}}{{{\hat{r}}_{EX}(t)}}^{2}}{\left. {\overset{\hat{}}{\beta}(t)} \middle| {}_{{Cross}\text{-}{correlation}}{+ \rho} \right.},\delta_{\min}} \right\}}}},$obtaining the time-varying regularization factor used for iterativelyupdating the coefficient vector of the FIR filter; wherein δ^(opt)(t) isthe time-varying regularization factor; L is a quantity of coefficientsof the FIR filter; {circumflex over (σ)}_(y) ²(t) is the power of thesignal received by the microphone;${\overset{\hat{}}{\beta}(t)} = \frac{\left. {\overset{\hat{}}{\beta}(t)} \right|_{{Cross}\mspace{14mu}{correlatio}n}}{{{{\overset{\hat{}}{r}}_{ex}(t)}}^{2}}$is the effective estimation value of coupling factor; {circumflex over(β)}(t)|_(Cross-correlation) is a biased estimation value of thecoupling factor based on a cross-correlation technique; |{circumflexover (r)}_(ex)(t)|² is a square of an effective magnitude of acorrelation coefficient between an error signal outputted by the AEC andthe far-end reference signal; δ_(min) is a preset small real constantquantity, and δ_(min)>0; ρ₀ and ρ are preset small real constants, andρ>0, ρ₀>0, respectively; t is a digital-signal sample time index number;or, wherein the preset signal comprises a subband spectrum of a near-endspeech signal received by a microphone and a subband spectrum of afar-end reference speech signal inputted in an AEC; the coefficientvector of the FIR filter is a subband-domain coefficient vector of theFIR filter and the time-varying regularization factor is asubband-domain time-varying regularization factor; obtaining thetime-varying regularization factor used for iteratively updating thecoefficient vector of the FIR filter in a case that the coefficientvector of the FIR filter is used for processing the preset signal,comprises: obtaining a subband power spectrum of the near-end signalreceived by the microphone and an effective estimation value of asubband-domain coupling factor, respectively; according to the subbandpower spectrum of the near-end signal received by the microphone and theeffective estimation value of the subband-domain coupling factor,obtaining a subband-domain time-varying regularization factor used foriteratively updating the subband-domain coefficient vector of the FIRfilter in a case that the subband-domain coefficient vector of the FIRfilter is used for processing a preset signal; wherein according to thesubband power spectrum of the near-end signal received by the microphoneand the effective estimation value of the subband-domain couplingfactor, obtaining the subband-domain time-varying regularization factorused for iteratively updating the subband-domain coefficient vector ofthe FIR filter in a case that the subband-domain coefficient vector ofthe FIR filter is used for processing the preset signal, comprises:according to an Equation:${{\delta^{opt}\left( {k,n} \right)} = {{\max\left\{ {\frac{L_{s} \cdot {{\overset{\hat{}}{\sigma}}_{Y}^{2}\left( {k,n} \right)}}{{\overset{\hat{}}{\beta}\left( {k,n} \right)} + \rho_{0}},\ \delta_{\min}} \right\}} = {\max\left\{ {\frac{{L_{s} \cdot {{\overset{\hat{}}{\sigma}}_{Y}^{2}\left( {k,n} \right)}}{{{\overset{\hat{}}{r}}_{EX}\left( {k,n} \right)}}^{2}}{{\overset{\hat{}}{\beta}\left( {k,n} \right)}❘_{{Cross} - {correlation}}{+ \rho}},\delta_{\min}} \right\}}}},$obtaining the subband-domain time-varying regularization factor used foriteratively updating the subband-domain coefficient vector of the FIRfilter in a case that the subband-domain coefficient vector of the FIRfilter is used for processing the preset signal; δ^(opt)(k, n) is thesubband-domain time-varying regularization factor; {circumflex over(σ)}_(Y) ²(k, n) is a subband power spectrum of a signal received by themicrophone;${\hat{\beta}\left( {k,n} \right)} = \frac{\left. {\hat{\beta}\left( {k,n} \right)} \right|_{{Cross}\text{-}{correlation}}}{{{{\hat{r}}_{EX}\left( {k,n} \right)}}^{2}}$is the effective estimation value of the subband-domain coupling factor,{circumflex over (β)}(k, n)|_(Cross-correlation) is the biasedestimation value of the subband-domain coupling factor; |{circumflexover (r)}_(EX)(k, n)|² is the square of the effective magnitude of thecorrelation coefficient between the subband spectrum of the error signaloutputted by the AEC and the subband spectrum of the far-end referencesignal; δ_(min) is a preset small real constant quantity, and δ_(min)>0;ρ₀ and ρ are preset small real constants, and ρ>0, ρ₀>0; n is a signalframe time index variable.